Machine Learning Engineer, AI Studio
On-siteHyderabad, Telangana, India
Job Summary
Define component boundaries, acceptance criteria, and technical estimates with product and architecture partners. Design and implement maintainable Python, SQL, API, data, model, retrieval, agent-tool, and workflow components with clear contracts, testing, and documentation. Apply EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup, threshold, explainability, and error analysis where relevant. Build GenAI, NLP, RAG, and bounded agent components using structured output, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries, and recoverable failure behavior. Engineer batch or event-driven data, document, feature, embedding, label, and evaluation pipelines with schema validation, lineage, provenance, access control, and consistency checks. Define representative evaluation for model quality, uncertainty, retrieval, grounding, citations, task success, tool correctness, safety, latency, cost, and user impact. Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback, incident response, and runbooks; lead diagnosis of moderately complex failures. Apply security, privacy, Responsible AI, validation, auditability, human oversight, and applicable GxP controls; contribute reusable assets and guide Associate engineers on familiar work.
Required Qualifications
- Bachelor's/Master's degree
- 5 to 9 years of Computer Science, IT or related field experience
- Demonstrated ownership of at least one production software, data, ML, GenAI or automation component
- Strong hands-on proficiency in Python and SQL
- sound software-engineering and testing practices
- Strong capability in at least one of classical ML, GenAI/RAG/agents or MLOps/platform engineering
- working knowledge of adjacent areas
- Independent problem solving
- sound component-level technical judgment
- Clear communication of assumptions, evidence, trade-offs, risks and support implications
- Strong collaboration with business SMEs, product, architecture, software, data, platform, evaluation and control partners
- Ownership, reliability and disciplined follow-through from design through production support
- Ability to guide junior engineers
- learn new tools through evidence-based experimentation
- Must be able to lift 50 lbs
Desired Qualifications
- Advanced ML and deep learning
- Experience with PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference or uncertainty estimation
- Advanced GenAI and knowledge systems
- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG or evidence verification
- Cloud, data and MLOps
- Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow or GitHub Actions
- Agents and human-AI workflows
- Familiarity with MCP-style integration, agent tracing, adversarial testing, durable workflows, permissions, human review, BI or process automation
- Regulated enterprise delivery
- Experience in healthcare, life sciences, GxP, validated systems or another regulated or high-impact environment
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