Top Machine Learning Engineer Jobs in Mumbai

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.

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.

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.

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.

Lead the design, development, and operation of enterprise Agentic AI and ML platforms. Build agent orchestration workflows, APIs, custom connectors, integrations, monitoring, observability, and reliable cloud-based systems. Establish LLMOps, MLOps, governance, security, and lifecycle-management practices; troubleshoot complex platform issues; lead technical prototypes; document architectures; and mentor engineers. The role focuses on scalable Agentic AI solutions using cloud, orchestration, and modern ML tooling.

Develop artist-facing and backend tools that extend SideFX Houdini and integrate with VFX and CG production pipelines. Build clean, testable, maintainable software using HOM, HDK, Python, and/or C++. Collaborate with artists, developers, and project managers across the software lifecycle, while emphasizing usability, documentation, UX, reliability, and adoption. Support procedural workflows, production tools, simulations, rendering, and distributed studio systems.

Build Python-based tools for layout, animation, matchmove, and previs artists working with complex CG scenes. Develop scene-management systems, asset and animation library integrations, and 3DEqualizer data import/export workflows. Collaborate with product owners and stakeholders in an Agile environment, contributing across design, development, maintenance, and pipeline integration. Work may include front-end and back-end development for high-end VFX and feature animation pipelines.

Develop artist-facing and backend tools that extend Autodesk Maya and integrate with VFX and CG pipelines. Build reliable, maintainable software using HOM, HDK, Python, C++, MEL, and Maya APIs; improve workflows, usability, and adoption; and collaborate with artists, developers, and project managers throughout the software lifecycle. The role also involves documentation, testing, deployment, support, roadmap planning, and innovation across distributed VFX production teams.

Leads strategy, roadmap, and end-to-end development of ML-powered generative AI products. Defines requirements, prioritizes backlogs, integrates ML models into scalable experiences, tracks product metrics, evaluates emerging technologies, and coordinates cross-functional teams spanning ML research, engineering, data, UX, and creative functions.

Lead enterprise data science solutions spanning machine learning, deep learning, Generative AI, Agentic AI, NLP, and RAG. Frame business problems, develop and evaluate models, design production AI workflows, and collaborate on deployment, monitoring, MLOps, governance, and Responsible AI. Provide technical leadership, mentor junior team members, manage stakeholder relationships, and establish data science best practices across the organization.

Develops enterprise data science and machine learning solutions across forecasting, classification, optimization, ecommerce, and market mix modeling. Responsibilities include sourcing and engineering data, building scalable models, communicating insights, collaborating with technical teams, creating automation solutions, and establishing data science standards and processes. The role also supports innovation involving generative and agentic solutions.

Leads AI and data science strategy, model development, technical exploration, deployment, and post-production performance management. Manages and mentors a team of data scientists, prioritizes projects and resources, partners with stakeholders, and promotes AI literacy. The role requires expertise in machine learning, MLOps, Vertex AI, generative and agentic AI, stakeholder engagement, and people leadership.

Develop scalable AI and machine learning solutions for forecasting, optimization, classification, ecommerce, and operational analytics. Partner with stakeholders and engineering teams to deploy, monitor, and improve models. Apply supervised and unsupervised learning, NLP, time series, and mathematical optimization techniques. Contribute to data science standards, documentation, knowledge sharing, and community practices while translating business needs into measurable outcomes.

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.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.

Leads targeted, end-to-end recruitment for niche Data, AI, and Data Science roles. Develops sourcing strategies, conducts proactive headhunting, maps talent markets, engages passive candidates, and builds technical talent pipelines. Partners with executive and engineering stakeholders, manages candidate relationships and offer negotiations, and provides market intelligence on skills, compensation, competitors, and talent availability. Requires sufficient technical familiarity to distinguish specialized data and AI skill sets during screening.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Research Engineer applying frontier machine learning and reinforcement learning research to production AI capabilities. Responsibilities include model design, training, evaluation, RLHF, multi-objective optimization, LLM post-training, reward modeling, auditing, and translating prototypes into reliable systems. The role also involves customer engagement, technical communication, and shaping research and product strategy.

Lead technical support for B2B customer deployments, diagnosing issues across ML models, applications, APIs, infrastructure, networking, and deployment environments. Investigate incidents, review logs and configurations, coordinate escalations with FDE, SRE, and Engineering teams, monitor deployment health, document root causes, communicate with customer stakeholders, and develop tools and dashboards to meet support SLOs.

Build foundational AI and machine learning capabilities for Twilio’s Customer Memory platform. Responsibilities include researching LLM orchestration, retrieval, personalization, and autonomous agents; designing rigorous experiments; and deploying production-grade ML systems at scale. The role requires end-to-end ownership, cloud-based services, high-volume or real-time inference, technical leadership, mentorship, and collaboration across globally distributed teams.

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.

Intermediate KYC analyst responsible for AML monitoring, KYC reviews, regulatory reporting, remediation project management, root cause analysis, and stakeholder engagement. Ensures compliance with Citi standards and local regulations, provides QA and commentary on returns, participates in regional initiatives, and drives process improvements using CitiKYC tools and data analysis.

Leads demand-side yield strategy across iion’s ad marketplace and owns strategy for Cognitiion, its AI-driven yield optimization platform. Diagnoses marketplace performance, directs Yield Analysts, designs experiments, defines optimization metrics, and converts findings into platform requirements. Partners with marketplace, commercial, data science, engineering, and AI leadership to improve revenue, efficiency, bidding, win rates, and traffic allocation.

Lead and oversee global KYC Operations team to deliver AML monitoring, governance, and regulatory reporting. Drive KYC program strategy, vendor coordination, process and technology improvements, training, project delivery, and ensure compliance with policies and regulations while managing teams and stakeholder relationships.