AI jobs in India — roles, pay, and what they ask for

An AI Automation Specialist wires a company's everyday work into automated workflows and drops LLM steps inside them: pull a lead from HubSpot, have Claude or GPT classify and draft, write back to Airtable or Zoho, escalate to a human when confidence is low. In India the hiring is spread across small product companies, agencies and consulting shops (Avalara, Beghou, Dentsu Merkle, Netomi, WebLineIndia, dozens of 10-50 person startups), and 14 of the 26 job postings collected were fully remote. The role exists because n8n, Zapier and Make made automation cheap enough that businesses now automate operations, not just IT — and because it is one of the few AI jobs where a fresher with a strong portfolio of working workflows can get hired without a CS degree or years of backend experience.

An AI Content Creator in India turns a script or a brand brief into finished video and images with generative tools: shot-level prompts in Kling, Veo / Google Flow, Higgsfield, Runway or Midjourney, a voiceover from ElevenLabs or an avatar from HeyGen, then a cut in CapCut or Premiere Pro with hooks, captions and 9:16 / 1:1 / 16:9 variants that a brand team can publish the same day. The hiring comes from brands and media houses rather than IT services — D2C beauty and fashion (Purplle, Dermatouch, Moksha Media), audio and news media (Kuku, Lokmat), gaming (Scopely), performance-ad studios (JuiceLabs AI, Zee Labs) and Amazon's product-imagery team — clustered in Mumbai and Bengaluru with a surprising Kochi pocket that wants Malayalam. It is genuinely open to freshers and to switchers from editing, design, film or mass-comm (11 of 25 job postings ask for 0-2 years and several say 'we care what you've made, not courses'), but be clear about the money: posting-stated pay runs 2-8 LPA at entry, with Rs 15,000-30,000 a month at the small studios, and a reel of finished pieces with consistent characters counts for more than any degree or certificate.

An AI-enabled Data Analyst pulls the numbers a business runs on: writing SQL against the warehouse, cleaning messy operational data in Excel or Python, building Power BI / Tableau dashboards, and turning what they find into a recommendation someone can act on this week. In India the hiring is dominated by global capability centres, banks and consulting firms (Synchrony, Target, American Express, MetLife, Amazon, Mastercard, U.S. Bank, Cognizant, EXL, Wipro, Guidehouse, Accenture) plus consumer startups like Navi, smallcase, Vedantu, Fam and RentoMojo, and 27 of the 35 job postings we analysed were open to 0-2 years of experience. The 'AI-enabled' part is where the role is heading, not where every JD already is: 7 of 35 job postings name AI fluency and only 5 ask you to actually use AI tools on your own analysis, while 28 of 35 want Excel and 26 want SQL — so build the boring stack first, then let the LLMs (drafting SQL, summarising free text, speeding up EDA) be the thing that separates you in a very crowded fresher market.

An AI Data Annotator produces and checks the human data that AI models learn from: labelling images, video, audio and text against a written guideline, and increasingly rating, ranking and red-teaming what a model wrote. In India the hiring now splits three ways — AI labs and enterprises (Amazon's AGI Data Services, Innodata, iMerit, TELUS Digital, Uber AI Solutions), global platforms paying by the hour (Outlier, Mercor, Turing, Handshake AI), and a large layer of Indian annotation vendors and BPOs (Macgence, Shaip, Fidel Softech, G-Labs) — and 26 of 34 job postings we analysed are open to 0-2 years with many accepting any graduate, so it is the most reachable AI job on this list. Be clear-eyed about the pay: posted monthly salaries run Rs 15,000-30,000 (about 1.8-3.6 LPA) and Indian-language platform rates start near $2/hour, so treat this as a paid entry point that teaches you rubrics, quality metrics and model failure modes — the ladder out runs through prompt, eval and annotation-QA roles, not through staying an annotator.

An AI Engineer in India ships the software that puts models into production: Python services that call LLM APIs, RAG pipelines over company documents, agent workflows that plan and call tools, and the deployment, evaluation and monitoring around all of it. Hiring is led by IT services and GCCs — Accenture, Infosys, TCS, Wipro, Cognizant, HCLTech, Bosch — alongside product startups, and Bengaluru plus Hyderabad account for 31 of the 54 job postings behind this page. The same job is advertised as AI/ML Engineer, Generative AI Engineer or Agentic AI Engineer, which is why this page tracks them as one role; it skews senior — 23 of 54 postings ask for 5+ years and only 4 are open to freshers — so a working backend, data or ML engineer switching in has the shortest path.

An AI Governance & Compliance Analyst decides whether an AI system is allowed to go live and on what conditions: keeping the AI use-case inventory, running risk assessments against the EU AI Act, NIST AI RMF and ISO/IEC 42001, writing the policies, control templates and playbooks other teams must follow, reviewing third-party AI vendors, and reporting residual risk to leadership. In India the hiring splits across Big-4 and IT-services consulting arms (EY, Deloitte, PwC AC India, TCS, Wipro, Infosys, Accenture), BFSI and GCC risk functions (Barclays, State Street, Nomura, Booking Holdings, Amgen), industrials and transport (Bosch, Alstom, thyssenkrupp), aviation tech (SITA) and a couple of product companies (AlphaSense, LeadSquared) — 22 India postings collected in a 60-day window. It is the clearest switcher role on this site: employers recruit from compliance, GRC, audit, risk and privacy rather than engineering — AlphaSense asks for someone 'AI-literate without being a developer' and only 1 of the 22 postings (Infosys' Sr. Responsible AI Analyst) wants Python — but two things are true and worth knowing before you start: certifications are a stated gate here, with IAPP AIGP named as mandatory or required in three postings, and no posting in this collection is open to 0 years, the lowest bars being 1 year (Booking Holdings) and 2 years (PwC AC India) while 16 of 22 sit at 5+ years at Manager, AVP, VP or Partner grade.

An AI Product Manager owns AI-powered features end to end: finding which business problems are worth solving with an LLM, writing the PRD, deciding how much autonomy an agent gets, and defining what 'good enough quality' means before launch. In India the hiring is split between product-led SaaS companies in Bengaluru (Hiver, SpotDraft, GoComet, Zoca, 100ms, SquadStack), global product firms (Docusign, Google, Siemens, Ford), regulated enterprises building in-house AI platforms (Innovaccer, Amgen, L&T Finance, BMC) and services/consulting arms (Capgemini, EPAM), with 23 of 34 job postings collected built around AI agents rather than plain chatbots. It exists because engineering can now build almost anything with a model, and someone has to decide what is worth building, how it will be measured, and when a wrong answer becomes a business problem — but 30 of 34 job postings ask for 2+ years of prior product ownership and 19 want 5+, so this is a role you arrive at after a few years of product, consulting or solutions work, not one you enter.

An AI QA / GenAI Test Engineer tests software that answers differently every time it is asked: they score model outputs for hallucination and faithfulness, check that a RAG answer is actually grounded in the document it cites, run prompt regression suites so last week's prompt edit does not quietly break this week's release, and probe for bias, unsafe output and PII leakage before a feature ships. The hiring splits three ways — product companies and GCCs in Bengaluru (Qualcomm, LeadSquared, MeltPlan, Kobie), services and conversational-AI firms in Pune and Hyderabad (Codvo, NationsBenefits, Deqode), and remote contracts testing US and EU AI products (TestUnity, Jinendra Infotech, Somnetics). One warning before you apply: in Indian postings 'AI QA' means two different jobs — testing an AI product, or using AI tools like Copilot and Cursor to test an ordinary one — and the title never tells you which, so read the bullets, not the headline. The bar is high and the natural candidate is a manual or automation QA engineer crossing over: 9 of the 15 postings collected ask for 5+ years outright and the wider search behind this page found 12 of 17 at that level. But the door is not shut — AIVantage in Ahmedabad (QA Intern - AI), Somnetics (remote, 0-2 years, Rs 3-6 LPA) and Kiyado in Calicut (two-month training programme, B.Tech) all hire below two years, roughly one opening in eight, and every one of them is a small firm or a tier-2 city rather than a metro product company.

An AI Search Optimization Specialist — the job postings say GEO or AEO — works to get a brand named and cited when people ask ChatGPT, Gemini, Perplexity or Google's AI Overviews instead of clicking ten blue links: writing a set of buyer-style prompts and tracking which sources the assistants quote, restructuring pages so an answer can be lifted cleanly, adding schema markup, deciding which AI crawlers may read the site, and earning mentions on the review sites and directories the assistants lean on. The hiring is small and new — we found 22 India job postings whose main purpose is this, from SaaS companies (G2, CultureMonkey, Graphy, Neo), edtech (Great Learning, CipherSchools), SEO agencies and Accenture, against a few hundred listings that merely mention the phrase — and it is SEO with a new surface rather than a new profession, so 20 of 22 still ask for classic technical SEO. Most full-time titles want 3+ years of SEO, but 6 of 22 are open at 0-2 years (three of them internships paying ₹6,000-15,000 a month), which makes it a realistic next step for an SEO executive or a content writer and a harder first job for a fresher.

An AI Security Engineer in India secures the AI other teams are shipping: building guardrail and detector libraries in front of LLM endpoints, threat-modelling RAG pipelines and agent tool permissions, running adversarial tests before a model reaches production, and writing the AI governance controls (OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO 42001) an audit will ask for. The buyers are enterprises and BFSI GCCs — Cognizant, Infosys, Wipro and Deloitte on the services side, HSBC, Societe Generale GSC, HDFC Bank, TransUnion, Danaher and Qualys in-house — with Bengaluru taking 12 of the 22 job postings behind this page; red teaming is one capability inside this job rather than the job itself, because only 4 of those 22 postings are predominantly offensive AI work and two of them never name the end client. It is also closed to freshers: not one posting in this sample is open at 0-2 years, the lowest bars are Thales and SIXT at 3-5, the mode is 5-10+ and HDFC Bank asks for 18+, so this is a move made by someone already doing security, or already doing AI/ML and willing to learn the attack surface.

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.

An AI Support Engineer in India runs the L1/L2/L3 desk for an AI product or platform: tickets arrive in ServiceNow, Jira or Zendesk, and the day is reading logs and traces in Grafana, Kibana or Application Insights, reproducing a failing API call with curl or Postman, deciding whether a bad answer is the model, the prompt, the data or the customer's integration, following or writing the runbook, and escalating to engineering with an evidence pack inside an SLA — often on rotational, weekend or US-hour shifts (9 of 26 job postings say so). The hiring comes from AI vendors standing up India support for their platforms (Together AI, Glean, C3 AI, Databricks, NVIDIA, interface.ai, WisdomAI), GCCs supporting internal AI platforms (Workday, FIS, Albemarle, WPP) and IT-services desks supporting a client's AI estate (GlobalLogic, Infosys, TCS, Unisys, PwC AC, Promaynov, Datamatics), clustered heavily in Bengaluru (14 of 26) with Pune, Delhi NCR and Mumbai behind. It is the most natural AI switch for application-support, production-support and IT-helpdesk engineers, and open to freshers only at the edges (3 of 26 postings accept 0-2 years; Workday's associate grade and Valiance's L1 seat want 1-2 years of any support work) — but be clear on pay: it tracks conventional support bands, not engineering ones, and because no AI-specific support title has a published salary distribution yet, the bands below were read for Technical Support Engineer and Production Support Engineer, roughly 3-4 LPA at entry, 4.5-9 LPA at 2-5 years and 8-20 LPA senior.

An AI Technical Trainer in India teaches Python, machine learning, data analytics and now generative AI to a cohort: a classroom or virtual session in the morning, a hands-on lab or Colab notebook after it, then assignments to grade, doubts to clear, a quiz or capstone rubric to write and a slide deck to update because the tools changed again. Two very different employers hire under the same title — skilling institutes, edtechs and university-embedded academies (Imarticus, upGrad, Itvedant, Red & White, SkillsNCR, Edunet Foundation, Revature) take trainers at 0-3 years, with 6 of 28 job postings asking for 0-2 years and two saying freshers with projects and a GitHub can apply, while IT-services and enterprise L&D teams (Accenture, HCLTech, Cognizant, Tredence, Evalueserve, Welspun) want 6-12+ years of hands-on AI plus training experience; 8 of the 28 postings are in tier-2 towns such as Kota, Jaipur, Valsad, Indore, Nagpur, Coimbatore, Chandigarh and Bhubaneswar, and three ask for Hindi as well as English. Be clear about the money: AmbitionBox puts a Data Science Trainer at Rs 3.8-5 lakh a year with 0-2 years and Rs 5-8 lakh at 2-5 years, posting-stated pay runs from Rs 20,000-30,000 a month at the small institutes to 4.2-8 LPA, and the switchers who do best are engineers and analysts who can already explain things and school or college teachers who put in the lab hours.

A Computer Vision Engineer in India trains detection, segmentation and OCR models on images and video, then squeezes them onto the hardware where the camera actually lives - an NVIDIA Jetson on a factory line, a drone, a CCTV box or an Android phone. The postings come from industrial inspection (Ripik.AI, ArcelorMittal, Frinks AI, Mindsprint), drones and robotics (Big Bang Boom, UMA Robotic, Skild AI), medical devices (Infosys, Meril), document and KYC checks (Airtel Africa, Digitap.ai) and IT services (TCS, Infosys, Quest Global), because cameras and cheap edge GPUs are now everywhere and 18 of the 26 job postings collected want the model optimised for that hardware, not just trained. It is more senior-heavy than ML engineering - 12 of 26 ask for 5+ years and 14 of 26 want C++ next to Python - but 5 startup and robotics postings take 0-2 years, so a fresher with a deployed, benchmarked detector has a real, if narrow, door.

A Forward Deployed Engineer sits inside the customer's problem: you fly out (or dial in) to a bank, a media company or a merchant, work out what they actually need, then build and ship the AI system in their environment — their data, their legacy integrations, their compliance rules — until it hits an agreed number in production. Indian hiring now runs on two tracks: an engineer track at AI-first startups and platform companies (Sarvam AI, Cartesia, DevRev, realfast, Deployment Inc, Razorpay, Handshake, Level AI), at frontier AI labs staffing India (OpenAI, ElevenLabs, LiveKit, Cursor, Databricks) and at IT-services firms (Accenture, Cognizant, Turing); and a strategist track — 7 of 35 job postings are titled Deployment Strategist or Field Engineer and screen for discovery, PRDs and customer adoption rather than a coding test. The role exists because enterprises can buy a model but cannot deploy one, and it pays well above standard backend work — but 23 of 35 job postings are in Bengaluru, and 30 set a floor of at least two years of shipped production software with 19 asking for five or more, so it suits working engineers moving into AI, not freshers.

A Machine Learning Engineer in India builds models and, more often than the title suggests, everything around them: preparing data, training and evaluating models, then packaging them into containers, deploying to AWS, Azure or GCP and watching for drift, latency and cost in production. Hiring splits into two tracks - applied ML at product companies (Amazon, Adobe, Conga, 6sense, Coinbase, Simplismart) and MLOps/platform work at IT-services and enterprise teams (TCS, LTIMindtree, Accenture, Straive, Qualys, SIXT), where 8 of the 31 job postings collected carry 'MLOps' in the title. It is the biggest AI job market in the country - LinkedIn alone lists 9,000+ ML engineer roles in Bengaluru - but not the easiest door: only 4 of 31 job postings accepted 0-2 years, so a fresher needs a deployed, monitored project rather than coursework.

A Prompt Engineer in India writes and versions the instructions that make an LLM behave: system prompts and few-shot examples for a document-extraction or support workflow, strict JSON schemas so downstream code can parse the output, a golden set of test cases, and a weekly loop of reading failures and fixing prompts. The hiring is led by IT-services and GCC firms (Accenture, Infosys, TCS, PwC AC India) plus conversational-AI and voice-bot startups in Bengaluru, NCR and Hyderabad, and the pay splits in two rather than sitting on a ladder — 4-8 LPA for the voice, content and enablement postings, 30-50 LPA for the rare engineering ones. Be clear-eyed about what you are aiming at: the title is thinning (Glassdoor India's own count fell from 5,995 to 5,229 in a month), 13 of these 32 postings carry no 'prompt' in the title, and the work is dissolving in both directions — down into conversation design and enablement, up into agent engineering — so treat prompting as your entry wedge and add evals, structured outputs, Python and agent workflows fast: that is the half of the split that pays.