Sr Machine Learning Engineer

Overview

Senior individual-contributor role building and scaling end-to-end machine-learning and generative-AI platforms, acting as a player-coach who sets platform strategy and technical standards.

What you'll do

• Engineer end-to-end ML pipelines covering data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion, using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks.
• Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC or event-driven APIs.
• Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency.
• Optimise performance and cost at scale by selecting appropriate algorithms, applying quantisation/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets.
• Instrument comprehensive observability, including real-time metrics, distributed tracing, drift and bias detection and user-behaviour analytics.
• Embed security and responsible-AI controls (data encryption, access policies, lineage tracking, explainability and bias monitoring) in partnership with Security, Privacy and Compliance teams.
• Contribute reusable platform components such as feature stores, model registries and experiment-tracking libraries, and evangelise best practices across squads.
• Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
• Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.

What you'll need

• 3-5 years in AI/ML and enterprise software.
• Comprehensive command of machine-learning algorithms, including regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques.
• Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
• Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
• Proficiency in Python and Java.
• Containerisation (Docker/K8s) and cloud (AWS, Azure or GCP) experience.
• Modern DevOps/MLOps experience (GitHub Actions, Bedrock/SageMaker Pipelines).
• Strong business-case skills, able to model TCO vs. NPV and present trade-offs to executives.
• Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.
• Master's degree in Computer Science or Information Systems, OR Bachelor's degree with 8 to 13+ years of experience in Computer Science, IT or related field.
• Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills.
• Ability to work effectively with global, virtual teams.
• High degree of initiative and self-motivation.
• Ability to manage multiple priorities successfully.
• Team-oriented, with a focus on achieving team goals.
• Ability to learn quickly, be organized and detail oriented.
• Strong presentation and public speaking skills.

Nice to have

• Experience in Biotechnology or pharma industry.
• Published thought-leadership or conference talks on enterprise GenAI adoption.
• Master's degree in Computer Science and or Data Science.
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.
• Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.).

Details

• Location: Hyderabad, India.