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. In India the hiring splits three ways — IT-services and consulting GenAI practices (Cognizant, Wipro, HCLTech, Deloitte, KPMG), hyperscalers and data platforms (AWS, Google Cloud, Databricks, Snowflake), and AI product startups hiring remote-India solutions engineers (Deepgram, Giga, Zenskar, Resilinc). 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; 11 of 21 job ads 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.
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.
Run a customer discovery → POC → pilot loop
16 of 21 job ads describe the same loop — discovery call, scoped POC on the customer's data, demo, handover. AWS wants you to 'embed with enterprise customer teams and deliver working prototypes'; Deepgram wants 'custom proof-of-concepts for prospects'. If you can only show finished products and no POC you built under a deadline, you fail the screen.
Deliver technical demos and answer RFPs
14 of 21 job ads ask you to build demo environments, tailor the story per audience and answer RFP/RFI documents — Orion and Bosch name RFPs outright, Unifize wants 'configuring software platforms and building tailored demos'. Practise a 15-minute demo that survives interruptions.
Explain how LLMs work and where they fail
13 of 21 job ads are explicitly GenAI/LLM roles, and your value is explaining tokens, context limits, hallucination, RAG-vs-fine-tuning and cost drivers to a customer who has read a headline. You are paid to say 'this use case will not work' before a pilot burns three months.
Translate business requirements into an AI solution design
12 of 21 job ads want you to run discovery and turn it into an architecture with success criteria — Orion: 'lead technical discovery sessions... design end-to-end architectures'; Zenskar: 'translating complex business requirements into structured workflows'; Unifize: 'translate abstract business needs into product workflows'.
Communicate AI trade-offs to stakeholders
11 of 21 job ads name presentation or stakeholder skills at executive level — Snowflake asks for 'outstanding skills presenting to both technical and executive audiences', Cognizant expects you to 'lead C-level discussions'. This is the capability that separates this role from a backend job, and it is graded in the interview.
Design scalable AI-backed systems
9 of 21 job ads are architect-titled or ask for architecture directly: modular AI platforms integrating LLMs and multi-agent orchestration (Cognizant), 'distributed systems at enterprise scale: API design, async patterns, event-driven architectures' (AWS). Be able to whiteboard latency, cost and failure modes for an LLM system on demand.
Integrate LLM APIs into an application
8 of 21 job ads want you personally writing the integration, not reviewing someone else's — AWS asks for 'production experience with LLMs', Google's AI Solutions Engineer wants 'full-stack Generative AI applications', Giga wants Python and tool calls. The hands-on job ads pay the most (Giga states 30-40 LPA at 2-3 years).
Use managed AI platforms (Bedrock / Vertex / Azure AI Foundry)
7 of 21 job ads are anchored to a managed platform — Bedrock, SageMaker, Knowledge Bases and Bedrock Guardrails at AWS; Vertex/GCP at Google Cloud; hyperscaler partnerships at Cognizant. Pick one cloud and learn its AI console, IAM and pricing properly; customers ask about quotas and cost, not model internals.
Build scheduled data pipelines
6 of 21 job ads sit on the data platform side — Google's AI Solutions Engineer opens with 'designing data pipelines for GenAI and ML models', and Databricks, Snowflake, Orion and Bosch expect you to talk fluently about ETL and warehouses. Most enterprise AI POCs die on data access, so this is where credibility is won.
Deploy an AI service to the cloud
6 of 21 job ads expect the POC to actually run somewhere — Giga wants you to 'set up CI/CD, monitor live usage', AWS lists Lambda/ECS/EKS, Cognizant wants deployment and monitoring standards. A demo that only runs on your laptop does not survive a customer pilot.
Train and evaluate classical ML models
6 of 21 job ads still want a data-science floor (Snowflake asks for 6 years as a data architect, scientist or engineer; Databricks wants 'core strength in either data engineering or data science'). You need enough to size a classical-ML alternative honestly when an LLM is the wrong tool — not to win Kaggle.
Write production-quality Python for AI work
Only 5 of 21 job ads name Python, but they are the ones worth targeting: Google Cloud asks for 'L400-level Python', AWS wants strong Python plus one of TypeScript/Java/Go, Giga wants Python and tool calls. Coding ability is what now separates a hired solutions engineer from a deck-only pre-sales person.
Build a multi-step agent workflow
5 of 21 job ads already ask for agentic work — multi-agent orchestration and agent lifecycle governance (Cognizant), discovery workshops 'for agentic AI deployment' (AWS), production voice agents (Deepgram) — and this is the fastest-moving requirement in the sample, so build one plan-act-observe agent with a human-approval step before your next interview.
Build a grounded RAG application with citations
Only 3 of 21 job ads name RAG explicitly, but they are the hyperscaler and architect roles (AWS: 'RAG pipeline design'; Google: RAG and fine-tuning; Cognizant: RAG in the platform stack) — grounded answers over a customer's own documents is the single most-requested POC, so you will build one in week two whether the JD says so or not.
Families: Product, business & communication · Programming foundations · LLM application development · Cloud, deployment & production · Data engineering & analytics · Machine learning & data science · Agents & workflows · Retrieval & knowledge systems
"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 — Named in 2 of 21 job ads (Cognizant's 12-year architect role and Google's applications role). 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 1 of 21 job ads (Docker's own solutions engineer role). Docker plus one managed deploy (Cloud Run, ECS, App Service) covers the 6 job ads that ask for deployment; K8s depth belongs to platform engineers, not the person running the POC.
- Big-data stack (Spark, Hadoop, Delta Lake) — Concentrated in the 3 data-platform job ads (Databricks, Snowflake, Bosch), all archived 2025 listings. Only worth learning if you are specifically 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 21 job ads prefers an MBA (Bosch), and none require a sales certification. Hiring managers screen on a POC you can demo and a customer story you can tell; a two-year degree is the slowest way to get either.
- Computer vision and other niche ML specialisations — 1 of 21 job ads (ParallelDots) is vision-led. 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 ads almost line for line.
- 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 startup loops go hands-on — Google asks L400 Python, AWS asks about multi-agent architectures and Bedrock, Giga asks about tool calls.
- 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.
- 4
Solution design / architecture
A live scenario — an enterprise wants a GenAI assistant on their documents — where you scope it, draw the architecture, choose RAG versus fine-tuning, and lay out phases, success metrics, risks and a rough commercial estimate. Senior loops probe governance, guardrails 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 0 of the 21 job ads 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?
Entry-level roles cluster around ₹6.7–40 LPA. Every band on this page is quoted from a named source with a link, and pay varies widely by city, company type and 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 91 focused hours — about 12 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 21 job ads behind this page, the most-requested capabilities are Run a customer discovery → POC → pilot loop (76% of ads), Deliver technical demos and answer RFPs (67% of ads) and Explain how LLMs work and where they fail (62% of ads). 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 have the most AI Solutions openings?
Bengaluru (11), Remote (India) (5), Chennai (2) and Hyderabad (1) — counted across the 21 job ads behind this page. Remote-India roles are counted separately where the ad said so.
Is demand for AI Solutions roles in India growing?
Direction is up: Naukri JobSpeak for July 2026 puts AI/ML roles at +33% YoY (the fastest white-collar segment, against +6% for IT services), and Indian IT majors have trained close to seven lakh employees in generative AI to staff the GenAI practices these solution roles sit inside. What is changing is the bar — hyperscaler solution jobs now ask for hands-on agentic work (AWS India names Bedrock, multi-agent orchestration, RAG pipelines, MCP tool servers and LLM-as-judge evaluation; Google Cloud asks 8 years and L400 Python), so slide-only pre-sales is thinning out. Caveat on this sample: 5 of the 21 job ads are archived 2025 listings kept for their JD text (Bosch, Databricks, Snowflake, Giga, HCLTech), so read them as evidence of what these employers ask for, not as open roles today.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 21 job ads 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.
AWSWhat it pays
How much demand
- 20,217'ai solutions engineer' jobs in India — a broad keyword match, so treat it as an upper boundglassdoor
- 512Solutions Engineer positions in India (startup side of the market)wellfound
- 400'AI Solution Consultant' vacanciesindeed
- 384'presales architect' jobs in India — the narrow, title-exact end of the same marketglassdoor
Where this role is heading
- Direction is up: Naukri JobSpeak for July 2026 puts AI/ML roles at +33% YoY (the fastest white-collar segment, against +6% for IT services), and Indian IT majors have trained close to seven lakh employees in generative AI to staff the GenAI practices these solution roles sit inside.
- What is changing is the bar — hyperscaler solution jobs now ask for hands-on agentic work (AWS India names Bedrock, multi-agent orchestration, RAG pipelines, MCP tool servers and LLM-as-judge evaluation; Google Cloud asks 8 years and L400 Python), so slide-only pre-sales is thinning out.
- Caveat on this sample: 5 of the 21 job ads are archived 2025 listings kept for their JD text (Bosch, Databricks, Snowflake, Giga, HCLTech), so read them as evidence of what these employers ask for, not as open roles today.
Capability percentages come from 21 job descriptions read in full on 24-08-2026. How we do this