Top Machine Learning Jobs in Hyderabad

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.

Develop and enhance scalable full-stack accounting and finance applications using Python, Django, FastAPI, and React. Build APIs, responsive interfaces, data models, and AI-driven features; integrate machine learning models and third-party services; optimize performance, reliability, security, and scalability; troubleshoot production issues; and contribute to continuous improvements in development processes and technology.

Conduct frontier AI research across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design experiments, datasets, prototypes, tooling, and evaluation frameworks; train and assess models; analyze results; ensure rigor and reproducibility; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical insights, contribute to publications or open source, and mentor engineers and researchers.

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.

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.

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.

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.

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.

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.

Develop and enhance ServiceNow CMDB, ITOM, and ITSM solutions with AI-driven automation, AIOps, GenAI, intelligent incident routing, and predictive insights. Build workflows, integrations, applications, and automation using JavaScript, Glide scripting, REST APIs, Flow Designer, Business Rules, and Script Includes. Configure Discovery and Event Management, translate business requirements into scalable solutions, collaborate with stakeholders, and optimize existing ServiceNow applications while following security, governance, performance, and scalability standards.

Build production-grade AI and machine learning security products using Go services and Python pipelines. Develop LLM-powered analysis, RAG, classification, structured extraction, and agentic AI capabilities; establish evaluation datasets, metrics, regression suites, and drift monitoring. Collaborate with security researchers to translate threats into actionable detections, and deploy cloud-native solutions using Docker, Kubernetes, and major cloud platforms.

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.

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.

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.

Own product initiative delivery from kickoff through release, coordinating Product, Engineering, Data, and Solutions. Track dependencies, risks, blockers, scope changes, timelines, testing, handoffs, and launch readiness. Maintain accurate project status and documentation while ensuring execution remains aligned with product intent. This hands-on role requires technical fluency, cross-functional communication, strong ownership, and daily overlap with an Asia-based technology team.

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.

Hiring for multiple software engineering opportunities in Hyderabad, including fresher and experienced positions across Java, testing, Python, DevOps, AWS, full-stack development, data science, AI/ML, cybersecurity, Node.js, and React.js.