Sr Machine Learning Engineer

Roles & Responsibilities:

• Engineer end-to-end ML pipelines

  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 for consumption by downstream applications.

• 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

  selecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantisation/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets.

• Instrument comprehensive observability

  real-time metrics, distributed tracing, drift & bias detection and user-behaviour analytics enabling rapid diagnosis and continuous improvement of live models and applications.

• 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

  feature stores, model registries, experiment-tracking libraries and evangelise best practices that raise engineering velocity 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.

Must-HaveSkills:

• 3-5 years

  in AI/ML and enterprise software.

• Comprehensive command of machine-learning algorithms

  regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques with the judgment to choose, tune and operationalise the right method for a given business problem.
• 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); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (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.

Good-to-Have Skills:

• Experience in Biotechnology or pharma industry is a big plus
• 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.

Education and Professional Certifications

• Master s degree with 6-11 + years of experience in Computer Science, IT or related field

OR

• Bachelor s degree with 8-13 + years of experience in Computer Science, IT or related field
• Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc. ) are a plus.

Soft Skills:

• 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.