Top Machine Learning Jobs in India

Design, fine-tune, and optimize LLMs (LLaMA, OpenAI GPT) using LoRA and quantization; build RAG pipelines with FAISS and hybrid retrieval; develop autonomous agents with LangChain/ADK; implement ML/DL models in PyTorch and TensorFlow; build scalable APIs (FastAPI/Flask) and deploy on AWS/Azure; add guardrails, monitoring, evaluation, and performance optimization; containerize and manage deployments with Docker and Git.

Design, train, fine-tune, and deploy NLP and generative AI models (including LLMs) and conversational agents. Implement MLOps practices for production deployment, monitor model performance, iterate improvements, and integrate AI solutions on cloud platforms (AWS/GCP) while collaborating with cross-functional teams.

Lead and mentor MLOps/AI engineers to design, build, and operate scalable ML workflows on Databricks and AWS. Apply PySpark, MLlib, and deep learning (PyTorch); integrate generative models; build model-serving APIs with FastAPI; automate infrastructure with Terraform; and implement CI/CD for ML deployments.

Lead design and implementation of AI/ML compiler and runtime software for RISC-V/NPU SoCs. Develop IREE/MLIR-based lowering, custom dialects and passes, code generation, quantization, and runtime integration. Optimize models for edge deployment across CPU, vector, matrix, and accelerator paths. Collaborate with hardware, firmware, FPGA and validation teams, build test/CI infrastructure, and provide technical leadership for performance tuning and customer enablement.

The role focuses on leading application development efforts with a strong emphasis on automation and generative AI while ensuring product quality and collboration across teams.

The role involves designing GenAI solutions with LLMs, optimizing RAG pipelines, developing applications, and integrating AI solutions into production systems.

This role involves designing scalable systems for Generative AI, integrating Azure OpenAI, and mentoring development teams in AI projects.

Design and deliver LLM-powered applications, build retrieval pipelines, and create evaluation frameworks. Collaborate with teams on innovative AI/ML solutions.

Design and validate performance models, optimize and debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and build tools to visualize performance data for ML/LLM inference on the Cerebras Wafer Scale Engine.

Architect and productionize ML/GenAI solutions on Databricks. Design RAG and agentic systems, build end-to-end MLOps pipelines, optimize training/inference, integrate cloud services, and support solution architects in technical presales and customer deployments.

Design and architect production-grade ML and GenAI solutions on Databricks, including RAG and agentic systems, MLOps pipelines, training/inference optimization, model monitoring, and customer-facing technical enablement and mentorship.

Design, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.

Design, develop, train, evaluate, and scale machine learning models (including generative AI). Extract business entities and insights from documents, build/maintain RESTful and GraphQL APIs, deploy via Docker and Azure, and follow DevOps/CI-CD practices while documenting work and collaborating with stakeholders.

The Senior Software Engineer will lead the design and development of AI-driven solutions, mentor junior engineers, and optimize AI/ML models for various applications.

Lead ABBYY's Document AI Data team, focusing on technical leadership and team management, driving data quality and scaling training data for AI models.

The Senior Software Engineer will develop and deploy AI/ML solutions, improve model performance, and collaborate with stakeholders to enhance customer experience.

Responsible for designing and building automated test frameworks for AI hardware/software, focusing on validation of machine learning models and CI/CD processes.

Lead and manage a team of ML engineers, drive AI service delivery, and oversee production model deployment while fostering a high-performance culture.

The Analog Layout Engineer will create and optimize physical layouts for IC designs, applying AI/ML tools and collaborating with engineers on simulations and verification.

Develop ML pipelines, evaluate model performance, and debug issues. Generate data formats for ML models and conduct data analysis and visualization.

Build and evaluate LLM-based systems for code generation, document understanding, retrieval, and agentic pipelines. Design evaluation frameworks, work with customers and founders to shape product and roadmap, and influence engineering culture and tech stack.

Design, train, optimize, and productionize deep learning models for speech/audio and computer vision. Build scalable training pipelines, perform model inference optimization, integrate models into production services, monitor performance, and collaborate with software and product teams to deliver low-latency, reliable ML features.

Design, optimize, deploy, and monitor ML models for in-vehicle audio/infotainment on embedded platforms. Build end-to-end ML systems, enable OTA updates and continuous training, optimize models for edge hardware, collaborate with DSP and data teams, implement MLOps/CI/CD, and ensure privacy, reliability, and real-time performance.

The Software Machine Learning Test Engineer will develop test automation for AI components, improve test infrastructure, and ensure software quality for machine learning models using Python and GitLab CI/CD.

The role involves designing and maintaining AI/ML platforms, deploying AI engines, managing model lifecycles, automating CI/CD pipelines, and ensuring system performance across cloud and on-premise environments.