Top Machine Learning Jobs in Hyderabad

Leads the architecture, implementation, and deployment of Azure-based data warehouses, pipelines, analytics platforms, and reporting frameworks. Provides consulting, solution design, presales support, regulatory analytics, and technical guidance to clients. Oversees data quality, governance, advanced analytics, and machine learning initiatives while mentoring analysts and consultants. Requires extensive experience with enterprise data architectures, ETL/ELT, dimensional modeling, client consulting, project leadership, team management, and financial services compliance.

Design, clean, analyze, and visualize data using SQL and Python; build Tableau dashboards; apply statistics and basic ML/DS concepts; follow software/data-engineering practices (version control, unit testing, high-performance code); work with structured and unstructured data and cloud environments.

Build production AI systems for finance teams, including per-customer anomaly detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with SQL and warehouse access in containerized customer deployments. Establish rigorous evaluation, anti-leakage, hallucination, grounding, and faithfulness controls, incorporate human confirmation checkpoints, and convert user feedback into durable model and system improvements.

Own core backend services for a finance AI platform, building reliable, configuration-driven computation systems over financial and operational data. Ensure results are traceable, reproducible, auditable, and correctly reconciled. Provide technical leadership through architecture, design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams. The role involves production Python, advanced SQL and data modeling, multi-tenant or customer-hosted deployments, and high standards for correctness and system reliability.

Develop generative AI applications, chatbots, intelligent agents, RAG pipelines, semantic search systems, and enterprise automation solutions. Build Python backend services and REST APIs, integrate commercial and open-source language models, use AI development frameworks, support AWS deployment, and perform prompt engineering, testing, debugging, and optimization. Collaborate with engineering and product teams to deliver maintainable, production-ready AI software.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Develop AI-powered product features for local discovery, including personalized recommendations, content curation, search, and ranking. Build data pipelines by scraping, cleaning, and structuring datasets for machine learning models. Collaborate with product, design, and engineering teams to integrate AI into the platform, while continuously improving machine learning capabilities and user experiences.

Seeking senior software engineers with proven expertise in at least one technical area, including AI/ML, LLMs, data, full-stack or backend development, automation, cloud infrastructure, and DevOps. Responsibilities include solving complex problems independently, collaborating across technologies, and delivering production-quality projects in a startup environment. Engagements may be project-based or ongoing.

The Customer Success Business Partner at Lyric drives customer adoption and retention for strategic accounts, leveraging expertise in AI and supply chain operations.

Build and maintain AI/ML applications, LLM-powered solutions, AI agents, agentic workflows, and Model Context Protocol integrations. Develop prompt, retrieval, tool-calling, and workflow automation systems; evaluate model quality, safety, and reliability; and support production deployment, monitoring, and continuous improvement. Collaborate with product, engineering, and business teams, while documenting architectures and implementation details.

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.

Designs, develops, deploys, and optimizes machine learning and data science solutions. Responsibilities include analyzing complex datasets, building predictive models, feature engineering, model evaluation, validation, data preprocessing, and managing end-to-end data science projects through production deployment. The role also involves collaborating with business and technical stakeholders, improving ML models and data pipelines, and communicating technical insights effectively.

Lead the design and delivery of AI/ML and Generative AI solutions, including RAG systems, LLM applications, prompt-driven agents, automation pipelines, and proof-of-concepts. Develop and fine-tune models, evaluate AI frameworks and cloud services, establish responsible AI practices, guide technical best practices, collaborate with stakeholders, and ensure enterprise security and compliance.

Leads the design, development, deployment, and optimization of scalable AI/ML solutions, including generative AI, LLM, NLP, and RAG applications. Drives architecture and solution design, develops production-ready models, collaborates with engineering and R&D teams on proofs of concept and deployments, ensures application scalability and reliability, and mentors engineers.

Develop Generative AI solutions using Python and FastAPI. Build and integrate machine learning, deep learning, NLP, LLM, prompt engineering, and RAG systems. Work with text embeddings and vector databases including FAISS, Chroma, and Pinecone. Implement model inference, deployment, REST APIs, and backend integrations. The role is hybrid in Hyderabad or Bangalore and requires candidates with 4.5+ years of relevant experience who are currently serving notice and can join within 15 days.

Leads the development of scalable, production-ready AI solutions using machine learning, generative AI, NLP, deep learning, neural networks, and cloud AI platforms. The role requires Python, TensorFlow, PyTorch or Keras, prompt engineering, Hugging Face, spaCy or NLTK, and data visualization tools. It also involves applying statistical modeling, working with Agile/Scrum methodologies, and collaborating with stakeholders.

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 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 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.

Design, deploy, and maintain scalable machine learning systems on GCP. Build training and inference pipelines, automate CI/CD and model deployment with Vertex AI, manage model versioning and rollouts, monitor drift and performance, and support production troubleshooting. Collaborate with data scientists, engineers, and stakeholders to operationalize models, improve platform reliability, and document production-ready workflows.