Top Machine Learning Jobs in Pune

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

Build and maintain data architectures, pipelines, warehouses, and lakes that ensure accurate, accessible, secure, and reliable data. Develop processing and analysis algorithms for complex, high-volume data and collaborate with data scientists to deploy machine learning models. Set technical strategy, advise senior stakeholders, manage risks and controls, guide technical direction, lead teams or specialist assignments, and drive continuous improvements aligned with business objectives.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Leads technical onboarding, enablement, and co-selling support for contact center partners. Designs scalable Zendesk, Amazon Connect, and AWS architectures; provides technical strategy, mentorship, solution guidance, demonstrations, proofs of concept, and implementation support. Develops partner training materials, ensures security and governance compliance, and promotes AI, machine learning, automation, and omnichannel contact center innovations. Collaborates with sales, engineering, vendors, and internal teams to drive partner success and accelerate solution deployment.

Lead design and implementation of an Analytic Foundation and scalable big-data services delivered via APIs. Build data pipelines, analytics and data models, responsive UIs, and production ML/data-driven applications. Ensure high-quality, well-tested code, mentor junior engineers, drive process improvements, and collaborate with product and design teams to deliver customer-focused data solutions.

Drive full-cycle sales for complex, high-value AI research workspace opportunities. Originate and close new business, support strategic renewals, expand stakeholder relationships, lead value-based workshops, and translate AI and research analytics capabilities into measurable customer outcomes. Engage senior decision-makers across enterprise, government, academic, and research organizations while developing pipelines, territories, business cases, negotiations, and long-term account growth.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

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.

Provides technical consulting and lifecycle services for Cisco networking customers. Responsibilities include Day-2 support, routing and switching troubleshooting, SDA/ACI architecture and optimization, customer workshops, technical documentation, solution adoption, and proactive problem resolution. The role also develops automation and AI-enabled delivery assets, mentors engineers, coordinates cross-functional teams, and advises customers and executives on network strategy, resilience, and security.

Leads the creation and scaling of AmeriSave’s India technology organization, including recruiting, engineering culture, architecture, software platforms, cloud infrastructure, AI initiatives, security, budgets, and global collaboration. The CTO partners with U.S. executives, oversees distributed engineering teams, drives delivery and operational excellence, and aligns technology strategy with business goals. The role requires extensive executive leadership, India team-building experience, software engineering depth, cloud expertise, and experience with AI and emerging technologies.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Lead architecture and development of agentic AI solutions for optical networks: design agent orchestration, memory, tool integration, guardrails, and human-in-the-loop workflows; convert processes into autonomous/semi-autonomous automation; document designs and best practices for scalable delivery.

Develop generative AI applications and agentic workflows using Python, LLMs, RAG, vector databases, and frameworks such as LangChain, LangGraph, and LlamaIndex. Build AI agents integrating APIs, tools, databases, and enterprise services. Develop FastAPI REST APIs, apply machine learning libraries, optimize prompts and model performance, evaluate responses, detect hallucinations, and secure AI-generated code. Collaborate within SDLC and Agile development practices.

Builds scalable ETL pipelines, cloud and data-lake integrations, and analytical tools supporting global supply chain planning and inventory optimization. Applies statistical methods, machine learning, AI-assisted development, and data visualization to portfolio segmentation, product lifecycle management, replenishment strategies, and decision support. Collaborates with stakeholders, leads complex data engineering projects, improves database performance, and promotes secure, governed, enterprise-wide data accessibility.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

Build and deploy production machine learning systems, including recommendation engines, semantic search, RAG pipelines, and LLM applications. Architect scalable APIs, workflows, background jobs, and data pipelines while optimizing reliability and performance. Collaborate with product and engineering teams, write tested modular code, conduct code reviews, and stay current with AI/ML and cloud-native technologies.

Own enterprise pipeline generation for strategic accounts across BFSI, manufacturing, and government. Design account-based outbound campaigns, conduct consultative discovery with C-level buyers, maintain CRM data quality, track conversion metrics, and partner with Account Executives on account planning. Mentor junior BDRs, support playbook development, and engage prospects around Rubiscape’s unified data, BI, ML, and AI platform.

Develop and deploy generative AI applications, integrate LLM APIs, perform text analysis using traditional machine learning and GenAI, and manage the machine learning lifecycle from problem definition through deployment and monitoring. Collaborate with cross-functional teams, translate business needs into technical solutions, evaluate new GenAI techniques, and document technical concepts.

Fine-tune and optimize small language models using Hugging Face, TRL, and adapter methods. Build MLOps pipelines, deploy models to edge and mobile environments, meet strict latency targets, and monitor production accuracy, latency, and hardware utilization. Evaluate model quality through benchmarking and custom evaluation suites while applying quantization, pruning, and knowledge distillation.

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.

Lead the technical roadmap, architecture, development, evaluation, and productionization of machine learning and AI solutions for search relevance. Build models using queries, product catalogs, and behavioral data; integrate scalable ML components into cloud systems; analyze search quality; mentor engineers; and influence cross-functional engineering and product decisions.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.