Top Machine Learning Jobs in Pune

Designs, builds, and operates Sensata’s Azure AI platform and production machine learning infrastructure. Responsibilities include developing reusable platform components, CI/CD pipelines, infrastructure-as-code, MLOps and LLMOps workflows, model monitoring, observability, security controls, incident response, and developer tooling. The role requires strong Python, Azure, container, automation, and production systems experience, with a focus on reliability, scalability, responsible AI, and cost optimization.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Design, build, test, deploy, and maintain innovative distributed software systems. Lead technical direction and feature roadmap for a critical international expansion function, optimize service performance and security, and operate in an agile environment. The role includes significant autonomy, cross-functional communication, and opportunities to hire and shape the engineering team. Candidates should have strong computer science fundamentals, modern software development experience, and familiarity with cloud, databases, frontend frameworks, machine learning, and DevOps technologies.

Leads enterprise architecture for scalable, multi-tenant SaaS systems and cloud-native platforms. Owns AWS architecture, distributed data pipelines, cost optimization, security, governance, and architectural standards. Drives GenAI and machine learning adoption across the software development lifecycle, builds self-service capabilities, guides engineering teams, conducts prototypes, and leads architecture reviews. Requires deep hands-on cloud, distributed systems, coding, infrastructure automation, AI/ML, and strategic technology leadership experience.

Analyzes US healthcare claims and related datasets to identify data-quality issues, patterns, coverage gaps, and product opportunities. Develops SQL queries, dashboards, validation processes, documentation, and test scenarios; investigates defects and performs root-cause and impact analysis. Collaborates with product, engineering, machine learning, engagement, and account teams to support healthcare data-product delivery, client data services, product enhancements, and proof-of-concept initiatives.

Leads emerging product innovation from opportunity discovery through prototyping, pilots, product-market validation, commercialization, and scale. Owns AI, data, analytics, automation, and platform product strategies; establishes experimentation and stage-gate governance; develops investment cases and go-to-market plans; and aligns cross-functional teams and executives. Manages product leaders, builds incubation practices, and drives measurable customer, revenue, efficiency, and strategic outcomes.

Design, build, deploy, and operate production AI/ML systems on Google Cloud. Develop clean, tested Python; evaluate model quality; manage cloud networking and security; and ensure data and models remain within client boundaries. Explain AI trade-offs to clients, collaborate with Google Cloud teams, and own outcomes from prototype through reliable production use.

Build and ship full-stack features across front-end and back-end systems. Develop Node.js and Python services, REST APIs, data models, and WordPress solutions using PHP. Create React interfaces, integrate internal and external services, manage AWS infrastructure and databases, and maintain code quality through testing, reviews, and documentation. Collaborate across teams, mentor developers, manage multiple agile projects, and independently deliver reliable technical solutions.

Build and deploy AI and machine learning models for business challenges, develop Generative AI and Agentic AI solutions using LangChain and LangGraph, leverage LLMs, create data visualizations and actionable insights, and collaborate with cross-functional teams while managing data science projects.

Build reinforcement learning environments for coding tasks, develop and deploy production AI agents, and shape technical direction. The role also involves leading engineering growth in a fast-paced startup. Candidates need 3–7 years of engineering experience, strong reinforcement learning fundamentals, experience with coding-focused reinforcement learning, simulated training environments, autonomous agents in production, and AI coding assistants. The position is remote in India with a 5.5-day workweek and Pacific-time overlap.

Lead the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.

Develop and deploy AI and machine learning solutions for DNV’s maritime business processes. The role covers stakeholder engagement, data analysis, technical architecture, experimentation, programming, testing, DevOps release cycles, and production integration. The successful candidate will identify new applied AI opportunities, collaborate with data scientists, developers, engineers, and domain experts, and contribute to business value through practical AI systems.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.

Lead technical direction and delivery for a core NIRA backend domain: design and ship high-volume web APIs, own architecture for distributed and queue-based systems, drive complex initiatives, mentor engineers, lead incident response, champion AI dev tools, and align cross-functionally to simplify systems and influence roadmap.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Develop quantitative models for cryptocurrency derivatives and spot markets, conduct statistical and econometric analysis, build pricing and risk models, and backtest systematic trading strategies. Collaborate with trading and product teams to validate and deploy models, monitor performance, analyze high-frequency data, and communicate research insights to leadership and trading desks.

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Designs and deploys production-grade AI agents, RAG systems, multi-agent workflows, SDKs, and scalable LLM applications. Builds secure knowledge architectures, integrations, MLOps and LLMOps pipelines, governance controls, and evaluation frameworks. Serves as a technical authority in client engagements, mentors engineers, supports presales and recruitment, and drives engineering standards across AI projects.

Leads the sales organization by setting strategy, managing targets, recruiting and developing sales staff, building strategic client and partner relationships, monitoring performance, forecasting revenue, managing budgets, and collaborating cross-functionally to drive market expansion and business growth.

Develop, deploy, and maintain scalable AI/ML pipelines and production systems, including LLMs and generative AI models. Collaborate with data scientists, software engineers, architects, and clients to integrate AI capabilities into products. Optimize model performance, latency, and cost on AWS, GCP, and Azure, using Docker, Kubernetes, CI/CD, monitoring, versioning, and other MLOps practices.

Train and deploy small, task-specific ML models for realtime English conversational-skill applications. Create datasets and task-specific evaluations, improve internal SOTA, develop ASR and speech-feedback metrics, iterate experiments, ship production systems, and collaborate cross-functionally while helping hire as the team grows.

Designs, develops, and operates enterprise automation and AI-enabled solutions using ServiceNow, Power Platform, machine learning, and agentic AI. Builds workflows, portals, applications, integrations, and CSM solutions; supports platform modernization, governance, security, testing, and production stability. Provides technical leadership, troubleshooting, vendor coordination, stakeholder collaboration, and team coaching while driving adoption of emerging automation technologies.