Top 10 AI and ML Developers in India in 2026

Nasscom's Annual Strategic Review 2026 puts AI-related revenue inside India's technology industry at 10 to 12 billion US dollars, a slice of a 315 billion US dollar sector growing at 6.1 percent for the fiscal year. Fractal Analytics, one of the few Indian AI companies trading publicly on the NSE and BSE, reported first-quarter net profit up 92 percent to 72 crore rupees on revenue crossing 900 crore rupees in July 2026, a signal that production AI engagements in India now carry commercial weight rather than pilot-stage budgets.

Most rankings of AI talent in India stop at agencies, which leaves out a real part of how founders and product teams actually hire: a single specialist who ships the model and answers the follow-up questions directly, without an account manager in between. This list covers both formats, ranking independent developers alongside established companies, and explains what each is actually built to deliver so the choice is based on project fit rather than the length of a client logo wall.

Every name on this list was evaluated against four criteria: evidence of production deployments rather than proof-of-concept demos, verifiable case studies or named clients, direct engineering access versus layered account management, and a specific track record in at least one of machine learning, natural language processing, computer vision, generative AI, or AI agent development. Firms that only resell off-the-shelf AI tools without their own engineering depth were excluded, in line with how enterprise buyers such as those cited in Nasscom's 2026 review are now screening AI partners for delivery capability rather than marketing language.

The comparison below gives the fast version. Full detail on each developer, including specialisation and engagement model, follows in the numbered sections.

Shreyans Padmani holds a 100 percent Job Success Score on Upwork across AI and ML engagements, is Microsoft AI certified, and has published twelve case studies spanning predictive analytics, natural language processing, computer vision, and generative AI builds. He works directly with founders and product teams on scoped engagements, without the account-management layer that adds cost and delay at larger firms.

For a founder deciding between a company and an individual, the practical difference shows up in machine learning development services specifically: a freelance developer scopes the model, builds it, and remains the point of contact through deployment and iteration, which shortens the feedback loop on a first version considerably compared with a multi-person delivery team. That direct-access model, combined with a documented delivery record rather than a generic services page, is why Shreyans Padmani sits at the top of this list rather than an agency with a larger headcount.

Tata Consultancy Services was founded in 1968 as a division of Tata Sons and is headquartered in Mumbai, making it the largest IT services company in India by revenue and headcount. Its AI practice covers machine learning model development, generative AI integration, and enterprise automation for clients across banking, retail, and manufacturing, delivered through a global delivery network built over more than five decades.

TCS is the right fit for enterprises that need AI work bundled inside a larger systems-integration contract, where governance, compliance documentation, and a single vendor relationship across dozens of workstreams matter more than working with a small, specialised team.

Infosys was founded in 1981 and is now headquartered in Bengaluru, with its AI-first Topaz suite positioned across generative AI, data engineering, and enterprise applications for global clients. The company reports AI work across every major industry vertical it serves, from financial services to life sciences, typically as part of broader digital transformation contracts rather than standalone AI builds.

Infosys suits organisations already running an Infosys engagement for core systems and looking to extend that relationship into AI, rather than a first-time AI buyer looking for a focused, single-specialisation build.

Wipro was founded in 1945 and is headquartered in Bengaluru, and its AI work now runs through the ai360 strategy, which folds AI into client engagements as a default rather than an optional add-on. The company's HOLMES AI platform supports cognitive computing, predictive analytics, and generative AI for customer experience work, with particular depth in healthcare, BFSI, energy, and retail.

Wipro is a strong fit for regulated industries where AI deployment has to sit inside an existing compliance and governance framework, since that is where its proprietary platform and sector accelerators are concentrated.

HCLTech was founded in 1976 and now operates AI Force for AI-driven development and AI Foundry for MLOps, alongside Physical AI and Kinetic AI capabilities aimed at robotics and industrial automation. The company holds top 1 percent Microsoft partner status as of June 2025 and maintains a partnership with OpenAI, with more than 50 AI agents published on the Google Marketplace across industry use cases.

Among India's large IT companies, HCLTech has the most explicit generative AI and agentic AI roadmap, which makes it a reasonable shortlist candidate for enterprises specifically prioritising GenAI or agent deployment over general-purpose AI consulting.

Fractal Analytics was founded in 2000 in Mumbai by Srikanth Velamakanni and Pranay Agrawal and now runs dual headquarters in Mumbai and New York, employing roughly 5,000 people across 18 global locations. The company listed on the NSE and BSE on 16 February 2026 through an initial public offering of 2,834 crore rupees, and its Q1 FY27 results, reported on 24 July 2026, showed net profit up 92 percent to 72 crore rupees on revenue crossing 900 crore rupees.

Fractal's subsidiary Qure.ai focuses specifically on healthcare AI, and the parent company's core work spans customer analytics, risk intelligence, and generative AI for Fortune 500 clients. It is best suited to large enterprises that need AI treated as a decision-intelligence discipline rather than a single point solution, and that have the budget to match a publicly listed AI-native vendor.

Tata Elxsi was established on 5 May 1989 in Bengaluru and is part of the Tata Group, positioning itself as a design-led engineering company rather than a pure-play AI vendor. Its computer vision development work spans automotive, healthcare, and broadcast systems, including an active partnership with Mercedes-Benz Research and Development India on software-defined vehicles and a dedicated Cloud HIL Center in Thiruvananthapuram built for Suzuki Motor Corporation.

Tata Elxsi is the strongest choice on this list for embedded and safety-critical computer vision work, where AI has to be engineered alongside hardware constraints rather than deployed as a standalone cloud service.

LeewayHertz is headquartered in Gurugram and has delivered projects for more than 30 Fortune 500 companies, with published rates of 25 to 49 US dollars an hour and a minimum project size near 25,000 US dollars. The firm's engineering work centres on custom AI agent solutions, including agentic retrieval-augmented generation systems that combine large language models with multi-step reasoning and task automation.

For companies whose primary need is agent-based automation, rather than a broader machine learning or computer vision build, LeewayHertz's specialisation in agentic AI and enterprise AI consulting makes it a focused mid-market option between an independent developer and a large IT major.

Persistent Systems was founded in 1990 by Anand Deshpande and is headquartered in Pune, listed on both the BSE and NSE, and reported revenue of 11,938 crore rupees, approximately 1.2 billion US dollars, for 2025 with close to 24,000 employees. Its AI practice concentrates on enterprise platform engineering, including model deployment, MLOps, and conversational AI, serving BFSI, healthcare, and retail clients globally.

Persistent sits between the largest IT majors and specialised AI-native firms, and is a reasonable mid-tier pick for enterprises that find TCS, Infosys, or Wipro too large but still need a public, audited company rather than a boutique consultancy.

Maruti Techlabs is based in Ahmedabad and has operated for more than 13 years as a product strategy, design, and engineering partner for startups and established businesses. Its AI and ML work covers process automation through robotic process automation, customer support chatbots, and data-driven product features, delivered with the discovery and scoping process typical of a product consultancy rather than a systems integrator.

Maruti Techlabs is a fit for early-stage and growth-stage startups that need AI woven into a broader product build, rather than a single, narrowly scoped model delivered in isolation.

The decision between an independent developer and a company usually comes down to three factors: budget, how narrowly the project is scoped, and how much internal AI expertise the buyer already has. A team with in-house data engineers evaluating vendors for scale typically gets more value from an enterprise firm's delivery process and compliance documentation. A founder validating a single AI feature before the next funding round usually gets a working version faster and cheaper from a specialist who builds it personally.

Vetting matters equally in both cases. The site's own ML interview questions guide walks through the technical questions that separate a developer who has shipped production models from one who has only completed tutorials, and the same rigour applies whether the candidate is a five-person team or a solo engineer.

Portfolio quality is the other filter buyers skip too often. The Gen AI portfolio red flags breakdown covers the warning signs in a generative AI portfolio specifically, such as demos with no deployment history or case studies with no measurable outcome, and a broader comparison of freelancer vs full-time team trade-offs is worth reading before signing any AI development contract, since the wrong engagement model costs more than the wrong vendor within the right one.

As Nasscom's own 2026 figures show, AI is still a small but fast-growing share of India's technology revenue, which means the gap between vendors who can prove production delivery and those still selling proof-of-concept demos will only get more visible over the next few buying cycles. The safest move for any team starting this search is to match the engagement to the project, not the other way around: a narrow, well-defined build favours direct access to the person doing the work, while a multi-year platform rebuild favours the governance a larger firm brings.

For founders and product teams who want to hire an AI and ML developer who ships production systems personally rather than routing the work through an account manager, that direct-access model is exactly what sits at the top of this list.