Top Artificial Intelligence Jobs in Pune

Develop generative AI applications using Python, LLM integrations, LangChain, LangGraph, RAG, prompt engineering, APIs, and vector databases. Build and integrate services with FastAPI or Spring Boot, use GitHub Copilot, Docker, CI/CD, and GCP, and ensure quality through unit and integration testing.

Design and develop enterprise generative AI and RAG applications using LLMs, embeddings, vector databases, hybrid search, and agentic workflows. Build ingestion, retrieval, evaluation, observability, and secure API services; optimize accuracy, latency, scalability, and cost. Integrate AI solutions with enterprise systems and collaborate with technical and business stakeholders. Apply prompt engineering, context management, guardrails, security, and responsible AI practices while troubleshooting and improving production applications.

Lead the architecture and development of production-grade multi-agent systems and AI-powered security chatbots. Build applied LLM workflows for alert triage, summarization, and automation; create evaluation harnesses; monitor and improve production behavior; establish standards for retrieval, context engineering, tool design, and safety; and embed security controls such as privacy, access controls, and prompt-injection defenses. Collaborate across data science, engineering, product, and platform teams to move AI capabilities from prototype to production.

Conduct end-to-end adversarial testing of LLM-powered systems, including jailbreaks, prompt injection, data leakage, tool misuse, and policy bypasses. Automate attacks with Python, build reusable attack libraries and regression tests, document reproducible security findings, and translate model failures into product requirements and customer-facing evidence. Support demos, security reviews, and sales conversations while tracking emerging AI security techniques.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

Build and own Supabase’s internal AI execution platform, including event-driven agent runtimes, durable state, human review, rollback, observability, evaluations, governance, and least-privilege tooling. Develop production agents, MCP integrations, infrastructure on Python and GCP, CI evaluation gates, operating metrics, and return-on-investment instrumentation. As the sole platform engineer, independently make architecture decisions, operate systems end to end, and ensure unsafe actions are structurally impossible.

Lead Supabase’s AI-native operating system by defining maturity models, company-wide standards, assessment processes, operating cadences, and transparency mechanisms. Manage two senior contributors focused on platform and enablement, drive adoption without direct authority, replace unnecessary meetings with automated reporting, integrate standards into onboarding, and measure whether AI agents reduce organizational coordination costs.

Architect and build production-grade agentic systems and shared AI platform services at enterprise scale. Develop agent orchestration, tool and MCP integrations, LLM gateways, evaluation frameworks, observability, safety controls, and cloud infrastructure. Drive reliability, cost optimization, AI-first engineering practices, technical standards, cross-team alignment, and mentorship while remaining hands-on with coding, architecture, and production debugging.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Lead the architecture and hands-on development of an AI-powered React application builder. Design scalable distributed systems, LLM pipelines, RAG and embeddings workflows, code-generation systems, publishing infrastructure, and AI guardrails. Improve observability, reliability, latency, safety, and fault tolerance while mentoring engineers and partnering with product, design, infrastructure, and security teams. This is an individual contributor role requiring production coding, system design leadership, and operational excellence.

Lead the design and delivery of AI context engineering and evaluation harness workstreams. Develop RAG, memory, token-management, ontology, MCP integration, sandboxing, orchestration, validation, and evaluation standards. Support pilot teams, define integrations with GitHub, Notion, and Slack, build YAML-based knowledge models and repo-native harness prototypes, and translate pilot evidence into company-wide best practices and technical standards.

Lead the design and implementation of agentic application-security workflows across the SDLC. Build AI agents for threat modeling, secure coding, CI/CD validation, testing, release risk assessment, and production feedback. Define security context models, guardrails, decision rules, human-review requirements, and escalation paths. Integrate workflows with engineering platforms, lead engineers across teams, validate outputs with security experts, and continuously improve agent quality, observability, and production readiness.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Leads the architecture, development, deployment, and scaling of enterprise AI solutions for finance automation. Integrates LLMs with SAP, Salesforce, and cloud platforms; establishes prompt engineering, CI/CD, security, governance, monitoring, and data practices. Mentors developers, leads technical strategy and vendor evaluations, presents to executives, and drives reliable AI platforms supporting financial processes, analytics, reporting, and global operations.

Build applied AI systems and prototypes using LLMs, RAG pipelines, vector databases, agentic workflows, structured extraction, and custom models. Design evaluation frameworks, measure accuracy and grounding, analyze errors, and iterate on solutions. Work with large structured and unstructured datasets using Python, SQL, and cloud data platforms. Partner with product, data, engineering, and business teams to translate ambiguous problems into measurable AI solutions and document architectures, prompts, runbooks, and results.

Lead the implementation and continuous improvement of an enterprise Project Risk Management operating model and Archer platform. Translate governance, compliance, contractual, and risk requirements into workflows, controls, decision trees, and assessment processes. Drive stakeholder adoption, standardization, governance forums, platform enhancements, and cross-functional collaboration across Legal, Privacy, Security, Compliance, Quality, and Risk teams. Support AI-enabled risk indicators, automation, evidence collection, and decision-support capabilities.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Build and lead the AI engineering platform for enterprise COBOL modernization. Responsibilities include designing transformer models for COBOL-to-Java/Python translation, developing high-performance code analysis engines, optimizing inference, architecting distributed cloud-native systems, and delivering reliable AI pipelines for large enterprise codebases. The role also includes hiring and leading engineering teams, shaping product strategy, and partnering with the CEO as a technical co-founder.

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.

Develop and maintain conversational AI applications using platforms such as Cognigy, Yellow.ai, Kore.ai, Lex, LUIS, Dialogflow, and IBM Watson. Build API integrations and complex application logic with Node.js or Python, optimize code, support iterative NLU/NLP improvements, define performance metrics, analyze chatbot data, and collaborate with business, product, and engineering stakeholders in Agile teams.

MBA summer associates join Roland Berger consulting teams for 8–12 weeks, working on real client projects across global offices. Interns apply strategic thinking, analytical and creative problem-solving, teamwork, entrepreneurial skills, and AI and data capabilities to complex business challenges. Outstanding performers may receive full-time offers.

Build and operate production-grade AI applications, agents, data products, pipelines, APIs, and intelligent workflows. Responsibilities include agent orchestration, retrieval, model integration, cloud and platform engineering, CI/CD, security, observability, evaluation, and production validation. The role requires developing scalable enterprise AI systems using Python and SQL, integrating models and business systems, and assessing data, retrieval, model, and agent quality.

Designs and leads scalable AI platform infrastructure, agent frameworks, orchestration layers, backend services, APIs, and cloud deployments. Optimizes LLMs and fine-tunes small language models using techniques such as quantization, distillation, RAG, caching, and prompt optimization. Implements MCP integrations, supports enterprise multi-agent workflows, evaluates emerging AI tools, partners with stakeholders, mentors engineers, and establishes engineering standards.

Supports development and maintenance of core AI infrastructure, including AI agents, LLM and prompt optimization, backend services, APIs, cloud deployments, and MCP integrations. Writes tested Python code, troubleshoots infrastructure, participates in code reviews and sprint planning, and documents technical processes. Requires Python, AI agent, LLM, backend, AWS, and MCP knowledge, plus a bachelor's degree and at least three years of experience.

Designs, builds, deploys, and maintains production AI applications, models, services, and pipelines for supply chain use cases. Develops intelligent agents, copilots, and RAG solutions integrated with enterprise systems and data. Establishes testing, evaluation, monitoring, deployment, and release practices while improving performance, reliability, cost, and explainability. Partners with technical teams and vendors, contributes to architecture and engineering standards, communicates trade-offs, and mentors engineers.