Sr Machine Learning Engineer
On-siteHyderabad, Telangana, India
Job Summary
Define user workflows, decision points, and measurable outcomes before selecting deterministic automation, classical ML, deep learning, GenAI, or RAG approaches. Own production architecture across data pipelines, models, retrieval systems, and APIs while leading hands-on development of inference services, agent tools, and workflow orchestration. Establish baselines, experiment design, leakage controls, and release thresholds; implement MLOps/LLMOps for lineage, reproducibility, CI/CD, canary releases, and drift monitoring. Coordinate security, privacy, responsible AI, and GxP controls to deliver governed, reusable assets with accountable ownership and measurable business value.
Required Qualifications
- Bachelor's/Master's degree with 8 - 13 years of experience in Computer Science, IT or related field
- Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, design review and end-to-end architecture
- Advanced statistics, ML and experimental design: EDA, feature engineering, supervised and unsupervised learning, predictive modelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified
- Deep learning, NLP and foundation models: Selection and production use of neural, transformer, embedding, vision, document and multimodal approaches, including fine-tuning versus prompting, latency, privacy and cost trade-offs
- GenAI, RAG, knowledge and agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarial evaluation and human control
- Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations
- Responsible AI and regulated delivery: Risk assessment, least privilege, threat modelling, red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicable GxP controls
- Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome
- Strong hands-on proficiency in Python and SQL, with experience designing production software, services and evaluation pipelines
- Advance capability in at least one role-defining pillar—Applied ML, GenAI/RAG/agents or ML platform/MLOps—plus credible depth across the production lifecycle
Desired Qualifications
- Advanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival methods or drift-aware retraining
- Advanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or LoRA, fine-tuning, distillation, quantization, routing, cascades or inference optimization
- Cloud, platform and AI operations: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code, MLflow, Airflow, Kubeflow, observability and FinOps
- Human-AI and regulated delivery: Experience with review, correction, approval, accessibility, uncertainty communication, workflow automation and GxP-relevant or validated systems
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