Top Machine Learning Jobs in India

Lead architecture for software products across cloud-native domains (AWS), drive technical governance, evaluate and adopt emerging technologies (AI/ML/LLM), produce and review secure production code, guide integration and service design, mentor teams, and influence product and operational decisions.

Design, build, deploy, and operate AI-powered full-stack applications following an AI Development Lifecycle. Implement frontend (React/Next.js/TypeScript) and backend (Node.js/Python) services, integrate LLMs, RAG, vector search, and MLOps/CI-CD pipelines, ensure security, observability, and production readiness, and mentor engineers.

Leads credit card risk analytics initiatives covering originations, collections, recoveries, payments, delinquency, stress testing, and portfolio performance. Develops predictive models, forecasting tools, monitoring frameworks, dashboards, and reporting for senior management. Extracts and analyzes data from enterprise databases, evaluates risk trends and root causes, supports regulatory and data lineage requirements, manages pilot rollouts, leads cross-functional projects, and mentors analysts.

Manage and mentor a team of software engineers and junior managers to design, build, and deploy high-quality Java microservices. Ensure architecture, security, scalability, and cloud readiness (modernization, IaC). Act as technical representative across teams, resolve technical roadblocks, drive DevOps/AI/ML adoption, collaborate with product owners, and oversee delivery, testing, and production migration.

Leads data engineering teams and provides technical direction for scalable, reliable data platforms and solutions. Oversees Agile delivery, backlog planning, operational support, quality standards, technical debt reduction, and stakeholder communication. Coaches engineers, sets performance goals, drives innovation, and guides adoption of data, cloud, containerization, DevOps, artificial intelligence, and machine learning technologies.

Designs, implements, validates, and deploys machine learning models and data pipelines. Develops data ingest/validation/cleaning/monitoring, evaluates vendor solutions, authors technical documentation, and mentors junior engineers.

Investigate AML alerts, enhanced monitoring reviews, and potentially suspicious activity requiring SAR/STR filings. Conduct screening and adverse media checks, support law enforcement and banking partner requests, ensure regulatory compliance, and manage time-sensitive workflows. Analyze transaction data and blockchain activity, identify financial crime trends, improve detection models and typology coverage, and collaborate with policy, legal, compliance, data, and technology teams. The role may also provide training and feedback to transaction monitoring analysts.

Lead design and implementation of autonomy and edge-AI software for air, land, and sea platforms. Develop tactical autonomy algorithms, integrate software with hardware, deploy and field-test capabilities, analyze mission data, mentor teammates, collaborate cross-functionally, and support customers and defense programs.

Deliver network design and optimization services and presales support for global telecom customers. Design complex end-to-end solutions across RAN, transport, packet core, and devices; implement solutions within scope, time, and cost constraints; consult with customer stakeholders; improve service, software, and product offerings; troubleshoot network performance; and share technical knowledge. The role focuses on 5G, radio network optimization, Cloud-RAN, analytics, and Ericsson network technologies.

Designs and optimizes end-to-end radio and network solutions for global telecom customers. Responsibilities include network design and optimization delivery, presales consulting, solution development, customer requirements analysis, implementation within scope and budget, stakeholder management, and knowledge sharing. The role focuses on 5G, RAN, Cloud-RAN, performance analysis, drive-test tools, data visualization, and troubleshooting across RAN, transport, packet core, and devices.

Develops, tests, and productionizes AI/ML solutions for Asset and Wealth Management. Responsibilities include gathering requirements, building machine learning models and data pipelines, applying NLP and supervised, unsupervised, and semi-supervised techniques, documenting solutions, conducting research, collaborating across teams, and coaching other AI/ML professionals.

Designs and develops AI and machine learning models, including NLP and computer vision systems, and deploys them into scalable production applications through APIs, microservices, and cloud platforms. Responsibilities include data preparation, model training, testing, debugging, performance optimization, and collaboration with data scientists and software engineers. The role requires strong programming, ML framework, cloud, SQL, and algorithm development skills.

Lead technical pre-sales and customer solutioning for AI and process intelligence initiatives. Translate business challenges into AI use cases, build ROI cases, prototype LLM and agentic systems, and execute proof-of-value projects. Design secure, scalable systems using RAG, tools, guardrails, and enterprise integrations. Present solutions to executives, support implementation and adoption, and specialize in business domains or industries.

Develop and deploy data science, machine learning, AI agents, and LLM applications for wealth management solutions. Design technical architectures and data pipelines, evaluate model quality and reliability, partner with technology, operations, vendors, and business teams, and lead automation and AI initiatives from concept through delivery.

Develop and deploy digital twins, multiphysics and structural models, and physics-informed AI to predict and optimize semiconductor process-structure-performance interactions. Apply computational materials science, ML, GenAI/LLMs, and sparse-data methods to accelerate technology development, yield, and reliability. Collaborate with global engineering teams to translate modeling insights into fab-technology co-optimization.

Develop and validate process, structure, and materials models using numerical methods and machine learning to enable predictive and prescriptive analytics for memory development and manufacturing. Implement and code multi-physics and process models, analyze results, present findings, and collaborate with cross-functional teams to drive decision support and automation.

Lead advanced structure and stress modeling using FEA and level set methods; develop constitutive and thermo-mechanical models; apply scientific computing and ML concepts; use Abaqus/ANSYS for analysis; document results, write reports and collaborate across teams; semiconductor industry experience desirable.

Designs, audits, and optimizes telecom networks across RAN, Transport, Core, OM and Services. Supports pre-sales with technical analysis, proposes scopes and estimates costs, documents service offerings, and applies automation and ML analytics to drive network performance and efficiency while managing stakeholder and financial aspects.

Design, build, and lead enterprise-scale observability and telemetry solutions across on‑prem and cloud platforms. Develop full‑stack microservices, monitoring integrations (Prometheus/Grafana/OpenTelemetry), and AI-powered observability features for anomaly detection, root-cause analysis, and automation. Influence standards, mentor peers, and collaborate with stakeholders to deliver scalable, secure production systems.

Leads and manages data science teams developing enterprise-scale AI and machine learning solutions for asset and wealth management operations and core technology. Translates business needs into scalable analytics and automation, promotes reusable tools and vendor solutions, partners on data architecture, and ensures compliance with risk, control, and governance requirements.

Own the vision, strategy, roadmap, backlog, and delivery of analytical, machine learning, and GenAI products. Translate business needs into requirements, lead Agile ceremonies, coordinate cross-functional teams, and guide products from discovery through production and adoption. Partner with data science, engineering, architecture, security, governance, and business stakeholders. Define success metrics, monitor performance and adoption, communicate progress and risks, and ensure responsible AI, privacy, security, and business-value considerations.

Perform statistical analyses and bias testing on traditional and ML/AI lending models, build mitigation algorithms, document results, support governance and regulatory exams, and present findings to stakeholders.

Design, build, and maintain SQL queries, Databricks pipelines, Alteryx and Python automations to produce governed self-service analytics in ThoughtSpot. Optimize performance, document data models and lineage, enforce data governance, deliver production-grade dashboards and recurring MI, translate stakeholder needs, lead complex analytics projects, and mentor team members.

Manage and verify client data for KYC and AML compliance, support client onboarding, resolve service issues, track exceptions, collaborate with stakeholders, analyze data, and improve operational workflows. The role requires strong analytical, communication, research, and problem-solving skills, along with knowledge of financial-services compliance and experience driving process improvements.

Manage pre- and post-trade lifecycle operations across asset classes, including reconciliations, cash coordination, onboarding, market and product expansion, and service enhancements. Coordinate Operations, Technology, Data Analytics, Risk, Compliance, and business stakeholders. Lead governance forums, track commitments and milestones, provide executive reporting, manage change, and drive automation, standardization, scalability, and operational excellence.