AI Solutions / Pre-sales Engineer
Also posted as: AI Solutions Engineer · Solutions Architect (AI) · Pre-sales Engineer (AI) · Solution Consultant (GenAI) · AI Consultant
An AI Solutions / Pre-sales Engineer is the technical person in the room while a customer is still deciding: they run discovery workshops, translate a messy business requirement into an AI solution design, build a working proof-of-concept on Bedrock, Vertex or Azure, demo it, and write the technical half of the RFP response. The design half now carries the role — 19 of 31 job postings are architect-titled or ask for architecture outright — and the hiring splits three ways: IT-services and consulting GenAI practices (Cognizant, Wipro, HCLTech, Deloitte, NTT DATA, KPMG), hyperscalers and data platforms (AWS, Google Cloud, Databricks, Snowflake), and the AI labs and product companies now putting pre-sales architects on the ground in India (Anthropic, OpenAI, ElevenLabs, Sarvam, Deepgram, Paytm). The role exists because enterprises are buying GenAI faster than they can evaluate it, and someone has to prove the thing works on the customer's data; 17 of 31 job postings ask for 5+ years and none are fresher-titled, so this is a role you move into from engineering, consulting or data work rather than start in.
- Across 76 AI Solutions / Pre-sales Engineer job postings in India, the most-requested capabilities are Run a customer discovery → POC → pilot loop (88%), Translate business requirements into an AI solution design (75%) and Communicate AI trade-offs to stakeholders (70%).
- Pay at 5+ yrs averages about ₹26.4 LPA (based on Solution Architect pay · verified across 2 salary sites: AmbitionBox, Glassdoor); employers offer ₹15–22.5 LPA (median of 16 job postings that state pay, all levels · Naukri, Indeed, Wellfound, other sites).
- At least 55 open roles in India — no portal could be counted in full, checked 04-10-2026.
- Hiring is concentrated in Bengaluru, Remote (India) and Delhi NCR.
- Postings read from LinkedIn 50%, company career pages 36%, other portals 9%, Wellfound 3% and Indeed 3%.
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Capabilities employers ask for
How often employers ask for each capability, measured across the job descriptions behind this page. Click one to see what "knowing it" means, how to learn it, and how to prove it.
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Run a customer discovery → POC → pilot loop
The pre-sales loop of discovery, scoped proof-of-concept and handover is the spine of this role. Paytm wants you to 'lead proof-of-concept (POC) engagements with clear success metrics', Deepgram wants discovery calls that turn into 'custom proof-of-concepts for prospects', Anthropic describes its Applied AI Architect as a 'Pre-Sales architect', and OpenAI follows customers 'from pre-sales discovery and solution evaluation through deployment'. You must be able to run a discovery call, scope a POC that proves one thing on the customer's data, and define what success looks like before you build.
Explore 5 practice tools →Translate business requirements into an AI solution design
Before anyone buys, you run discovery and come back with a design the customer can sign off. OpenAI wants you to 'identify, qualify, and prioritize use cases tied to meaningful business outcomes', E-Solutions wants architecture diagrams, sizing, effort estimation and an implementation roadmap, Atlas Copco wants business needs translated into 'application architecture, data flows, integration patterns', and Zenskar wants requirements turned into structured workflows. You must be able to take a fuzzy business problem and produce a scoped solution document with assumptions, risks and success criteria.
Explore 4 practice tools →Communicate AI trade-offs to stakeholders
Executive communication is a hard requirement here, and the audience is usually spending money. Cursor wants you 'equally comfortable coding alongside developers or presenting to CTOs', Anthropic wants you to 'translate requirements between technical and business stakeholders', Microsoft wants 'storytelling, and executive communication skills', and Lyzr AI wants experience reporting to CTOs and CIOs. You must be able to explain an AI trade-off to a technical lead and a business buyer in the same meeting, including the honest no.
Explore 3 practice tools →Explain how LLMs work and where they fail
These are GenAI roles first, and the job is explaining the technology to buyers credibly. AWS wants 'production experience with LLMs: advanced prompt engineering, multi-agent architectures, RAG pipeline design', Qdrant wants working knowledge of 'LLMs, embedding models, agentic frameworks', DevRev asks for prompt engineering, function calling and 'building evals', and Microsoft wants a strong grasp of Copilot and generative AI. You must be able to talk tokens, context limits, hallucination, RAG versus fine-tuning and cost per query, and say plainly when a use case will not work.
Explore 4 practice tools →Design scalable AI-backed systems
Architecture sits in the title or the requirements of these roles, and you usually draw and defend it rather than build it. AWS wants 'distributed systems at enterprise scale: API design, async patterns, event-driven architectures', ZEISS wants end-to-end GenAI architecture 'covering data pipelines, RAG, model orchestration, LLM integration', Anthropic wants you to help customers integrate Claude into their stack, and NTT DATA wants feasibility assessed across integration, security, privacy and cost. You must be able to whiteboard an AI system and defend its latency, cost, failure modes and integration effort.
Explore 4 practice tools →Build a multi-step agent workflow
Agentic work is everywhere in this sample, often in the job title. Paytm is hiring an 'AI Agentic Solutions Architect', Google Cloud wants a 'builder-consultant to code, debug, and jointly ship agentic solutions', TCS wants 'task-oriented agents, planners, tool-using agents', and Google's AI Customer Engineer names LangGraph, Semantic Kernel and the Agent Development Kit. You must be able to build one plan-act-observe agent with tool calls and a human-approval step and demo it live.
Explore 4 practice tools →Deliver technical demos and answer RFPs
Demos and proposals are what separate this role from the engineer who builds the system afterwards. Sarvam's principal architect builds 'demo frameworks, proposal templates, objection-handling guides', Metadome.ai wants 'live demos on customer data within days, not weeks', Kapalins wants comfort 'improvising through unscripted technical questions', and Dhan AI wants 'practical prototypes and demos rather than relying only on slides'. You must be able to give a short demo that survives interruptions and write RFP answers you can defend.
Explore 3 practice tools →Deploy an AI service to the cloud
Postings expect you to know where the system will run, usually by naming clouds rather than demanding operations work. AWS lists Lambda and AWS CDK, Snowflake and Databricks want AWS, Azure and GCP, Microsoft names Azure AI and Copilot Studio, and Globant asks for an Azure AI Engineer certificate. You must be able to put a demo behind a public URL on one cloud and talk credibly about running it inside a customer's environment.
Explore 4 practice tools →Train and evaluate classical ML models
A classical ML or data-science floor shows up mainly on the data-platform and consulting side. Anthropic offers 'a background in machine learning or data science' as a route in, AWS wants hands-on SageMaker, Databricks wants breadth across 'data engineering, data warehousing, business intelligence, AI, ML', and Golden Opportunities wants the 'end-to-end AI/ML development lifecycle'. You must be able to size a classical-ML option honestly against a GenAI one and explain how a model is trained and evaluated.
Explore 5 practice tools →Write production-quality Python for AI work
Python is named by the employers who expect you to build what you demo. OpenAI wants you to 'prototype, explain technical tradeoffs, and work confidently with APIs, SDKs, and languages such as Python or JavaScript', ElevenLabs wants Python with 'common integration patterns', AWS wants strong Python plus another language, and EXL wants async Python. You must be able to write the POC yourself rather than borrow an engineer the night before the demo.
Explore 4 practice tools →Integrate LLM APIs into an application
Postings expect you to understand how LLMs plug into an application, and some expect you to write that code yourself. Globant lists 'Python, FastAPI, LLM, LangGraph, RAG, APIs, Azure/OpenAI' as mandatory, Kapalins wants hands-on familiarity with OpenAI, Anthropic, Azure OpenAI, Bedrock and Vertex AI, and ZEISS India owns architecture that runs from RAG to 'LLM integration'. You must be able to call an LLM API with tools and structured output, handle its failures, and explain the integration to a customer's engineers.
Explore 4 practice tools →Apply responsible-AI and data-protection basics
Governance is a real requirement in some of these postings, and it is what enterprise buyers ask about. TCS wants 'guardrails for responsible AI, including bias mitigation, hallucination control, access controls, and auditability', Databricks wants you to 'guide customers through security blockers and concerns', E-Solutions wants RBAC, IAM, encryption and data privacy, and NTT DATA lists Responsible AI beside security and privacy. You must be able to answer where data is stored, who can see it and how outputs are audited, before the customer's security team asks.
Explore 3 practice tools →Use managed AI platforms (Bedrock / Vertex / Azure AI Foundry)
A named managed AI platform appears in only some postings, and when it does the ask is specific. AWS names Amazon Bedrock, SageMaker and Knowledge Bases for Bedrock, Accenture wants 'expertise and certification in cloud AI platforms' across Vertex AI, Azure AI and Bedrock, KatalAiser wants an understanding of Azure OpenAI, Bedrock or Vertex AI, and EY lists Azure AI Foundry. You must be able to stand up a model on one platform's console and answer what it costs and where the customer's data sits.
Explore 3 practice tools →Families: Product, business & communication · Programming foundations · Agents & workflows · Cloud, deployment & production · Machine learning & data science · LLM application development
"AWS" on a JD is not "learn AWS"
The words employers write, translated into what they want you to be able to do for this role.
Skip, for now
- Fine-tuning / LoRA — Still named in only 2 of 31 job postings (Cognizant's 12-year architect role and Google's GenAI applications role) — the ten newest postings added none. Your job is to advise when to fine-tune versus retrieve; be able to explain the trade-off and cost, and skip running PEFT jobs until a customer actually needs one.
- Kubernetes — Appears in exactly 1 of 31 job postings — Docker's own solutions engineer role, and that entry is a two-line search snippet whose K8s tag comes from the product, not from a fetched JD. Docker plus one managed deploy (Cloud Run, ECS, App Service) covers the 8 job postings that ask you to ship something; K8s depth belongs to platform engineers, not the person running the POC.
- Big-data stack (Spark, Hadoop, Delta Lake) — Only 2 of 31 job postings ask you to program it (Databricks and Snowflake, both archived 2025 listings); Orion and NTT DATA name those platforms as vendors you must discuss, not code. None of the ten newest postings mention it at all, so learn it only if you are targeting a data-platform vendor — for GenAI solution roles, SQL plus one warehouse is enough.
- An MBA or a stack of sales certifications — Exactly 1 of 31 job postings prefers an MBA (Bosch, an archived listing), and none require a sales certification. Hiring managers screen on a POC you can demo, an architecture you can defend and a customer story you can tell; a two-year degree is the slowest way to get any of the three.
- Computer vision and other niche ML specialisations — Vision shows up in 2 of 31 job postings and neither is a vision role on the evidence we have — ParallelDots is a one-line Indeed snippet inferred from what the company sells, and Cognizant lists CV alongside NLP, forecasting and recsys as domains to be conversant in. Broad GenAI fluency plus one cloud beats deep CV here; specialise only after you land in a vertical that needs it.
A customer-style POC in a box: discovery doc, working demo, RFP response
Pick one vertical you can speak to (insurance claims, hospital discharge summaries, manufacturing QMS) and run the full pre-sales loop on yourself. Write a two-page discovery document with the business problem, current process, success metric and data sources; then build the POC — a grounded RAG assistant over that vertical's public documents, with citations, an escalation path, and one agent step that calls a tool (create a ticket, look up a policy) — deployed on Bedrock, Vertex or Azure AI Foundry with a public URL. Package it the way a solutions engineer would: a 15-minute demo script with a persona-specific narrative, a one-page architecture diagram, a cost-per-query estimate, and a written response to five hard RFP-style questions (data residency, hallucination handling, integration effort, rollback, pricing). This mirrors the Orion, AWS, Deepgram and Unifize job postings almost line for line.
Start from One vertical you can speak to, with a discovery doc and demo you write yourself — no customer access needed
- Discovery doc states one measurable success criterion (e.g. 'answers 80% of tier-1 queries with a cited source') and the POC is measured against it on a 30-question eval set
- Live demo runs from a public URL in under 15 minutes, including one deliberate failure case you explain rather than hide
- Architecture diagram shows the cloud services used, where customer data sits, and the cost per 1,000 queries with your assumptions written down
- Written RFP answers cover data residency in India, guardrails/PII handling, integration effort in person-weeks, and what happens when the model is wrong
- A five-minute recorded walkthrough aimed at a non-technical buyer, not an engineer
What the interviews look like
The rounds you'll actually face, in the order they usually come.
- 1
Screening
Recruiter or hiring manager checks years of customer-facing technical experience, which cloud and which AI stack you have actually deployed, verticals you know, and comfort with travel/client sites. Have a link to a POC you can demo and one sentence on a deal or pilot you influenced.
- 2
Technical / take-home
Either a live technical conversation (LLM APIs, RAG design, cloud services, integration and data access) or a take-home POC on a supplied dataset. Hyperscaler and lab loops go hands-on — Google asks L400 Python, AWS asks about multi-agent architectures and Bedrock, Anthropic and ElevenLabs expect you to be comfortable in Python and the product's API.
- 3
Demo / customer simulation
The round that decides it: present your POC or a mock solution to a panel playing customer roles, then handle objections on cost, hallucination, data privacy and 'why not just use ChatGPT'. They score narrative structure, whiteboarding and how you behave when you do not know an answer — Paytm's posting names 'skepticism stemming from prior enterprise AI failures' as part of the job.
- 4
Solution design / architecture
Now the heaviest round in most loops. A live scenario — an enterprise wants a GenAI assistant on their documents — where you scope it, draw the architecture across models, data, integration, security and evaluation, choose RAG versus fine-tuning, and lay out phases, success metrics, risks and a rough commercial estimate. Senior loops probe governance, guardrails, how you would measure the thing in production, and what you would refuse to promise.
- 5
Culture / stakeholder
Discussion with sales leadership or delivery about working alongside account executives, handing a POC to a delivery team, managing several deals at once, and honesty under pressure to over-promise. IT-services and consulting firms also check domain comfort (BFSI, healthcare, manufacturing).
What people ask before choosing this role
Can a fresher get an AI Solutions / Pre-sales Engineer job in India?
Realistically, no — not straight away. Only 1 of the 76 job postings behind this page accept 0–2 years; most want people who have already shipped software or run projects. It is a strong second move rather than a first job.
What is the salary of an AI Solutions / Pre-sales Engineer in India?
Pay at 5+ yrs averages about ₹26.4 LPA (based on Solution Architect pay · verified across 2 salary sites: AmbitionBox, Glassdoor); employers offer ₹15–22.5 LPA (median of 16 job postings that state pay, all levels · Naukri, Indeed, Wellfound, other sites). Not every posting states pay, and pay varies widely by city and by whether the employer is an IT-services firm, a global capability centre or a product startup.
How long does it take to become an AI Solutions / Pre-sales Engineer?
The six capabilities employers ask for most add up to roughly 101 focused hours — about 13 weeks at 8 hours a week, if you are starting from zero on all of them. Most people are not: the self-check on this page works out what you can skip, which is usually a large part of it.
What skills do you need for an AI Solutions / Pre-sales Engineer role?
Across the 76 job postings behind this page, the most-requested capabilities are Run a customer discovery → POC → pilot loop (88% of postings), Translate business requirements into an AI solution design (75% of postings) and Communicate AI trade-offs to stakeholders (70% of postings). Note these are capabilities, not tools — employers write tool names, but what they are buying is the ability to do the work.
Which cities in India post the most AI Solutions jobs?
Bengaluru (38), Remote (India) (10), Delhi NCR (8) and Mumbai (7) — counted across the 76 job postings behind this page. Remote-India roles are counted separately where the posting said so.
Is demand for AI Solutions roles in India growing?
Naukri JobSpeak put AI/ML hiring up 31% year on year in August 2026 against 14% for white-collar hiring overall, with the fastest rise at 13-16 years' experience (+87%), the senior bands where AI solution roles sit. This role's own sample matches: 41 of the 57 job postings collected here with a posted date since 1 September ask for 5+ years and only 2 are open to 0-2 years, and vendor pre-sales postings (ServiceNow, Databricks) now ask for hands-on agentic AI work rather than demo skills alone.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 76 job postings behind it asked for one by name. What they ask for is evidence you can do the work — a public repo, a deployed project, something a hiring manager can open. That is what the path on this page is built to produce.
Companies with this role open in India
A sample of employers we saw hiring for this role — IT services, global capability centres, product companies and startups. Each links to one of the company's postings for this role, checked open on 04-10-2026.
What it pays
most earn ₹4–7 LPA (base pay) · based on Pre-Sales Engineer pay · verified across 2 salary sites: AmbitionBox, Glassdoor
based on Pre-Sales Engineer pay · verified across 2 salary sites: AmbitionBox, Glassdoor
based on Solution Architect pay · verified across 2 salary sites: AmbitionBox, Glassdoor
The median of 16 job postings that state pay, all levels · Naukri, Indeed, Wellfound, other sites — not every posting states pay, so this is a sample.
Across all levels: the middle half earns ₹18.5–34.7 LPA · Glassdoor · Solution Architect pay
How much demand
What each job portal shows for this role's title — the readings behind the openings figure above.
Where this role is heading
- Naukri JobSpeak put AI/ML hiring up 31% year on year in August 2026 against 14% for white-collar hiring overall, with the fastest rise at 13-16 years' experience (+87%), the senior bands where AI solution roles sit.
- This role's own sample matches: 41 of the 57 job postings collected here with a posted date since 1 September ask for 5+ years and only 2 are open to 0-2 years, and vendor pre-sales postings (ServiceNow, Databricks) now ask for hands-on agentic AI work rather than demo skills alone.
Capability percentages come from 76 job descriptions read in full on 03-10-2026. How we do this