ML Engineering Lead at Saris AI tackling multi - modal AI systems in banking. Drive technical direction and build high - performing teams in an early - stage startup environment.
About the role
- AI/ML Engineer building data science and AI solutions for Pharma and MedTech clients on Azure. Collaborating with teams to deliver end-to-end machine learning projects.
Responsibilities
- Build end-to-end ML and data science pipelines on Azure — ingestion, feature engineering, training, evaluation, and deployment.
- Develop LLM-powered solutions including RAG pipelines, prompt-engineered workflows, and agentic systems using Microsoft Agent Framework, Semantic Kernel, and Azure AI Foundry.
- Work with Pharma and MedTech data — commercial, clinical, real-world evidence, HCP/HCO, patient journey — to deliver predictive and generative use cases.
- Implement and integrate MCP tools and A2A-style agent collaboration patterns into client offerings.
- Operationalize models and agents using Azure ML and Azure AI Foundry — versioning, monitoring, observability, and responsible-AI guardrails.
- Collaborate with client stakeholders to translate business problems into solutions and contribute to POCs and proposals.
Requirements
- Bachelor's or Master's in CS, AI/ML, Data Science, Statistics, Applied Math, or a related quantitative field.
- Strong foundation in mathematics, probability, statistics, and linear algebra.
- Solid grasp of classical ML — regression, classification, clustering, tree-based models, evaluation, cross-validation.
- Proficient in Python (NumPy, pandas, scikit-learn) and working knowledge of both SQL and NoSQL.
- Hands-on exposure to LLMs and GenAI — prompt engineering, embeddings, vector stores, RAG.
- Familiarity with at least one agentic framework (Microsoft Agent Framework, Semantic Kernel, LangChain/LangGraph) and awareness of MCP and A2A protocols.
- Experience with Azure AI Foundry, Azure OpenAI, or Azure ML — via coursework, internships, projects, or prior work.
- MLOps basics — experiment tracking, model registry, CI/CD, Docker, Git.
- Exposure to Pharma, Life Sciences, or MedTech data and compliance (HIPAA, GxP).
- Deep learning (PyTorch / TensorFlow), OpenTelemetry, or a strong GitHub portfolio.
- Curiosity and a fast-learning curve — this field moves quickly.
- First-principles thinking and clear communication with clients and peers.
- Ownership and a pragmatic engineering mindset — production-ready, not notebook-only.
Benefits
- Real, production-grade AI work for leading Pharma and MedTech clients from day one.
- Mentorship from senior architects and exposure across classical ML, GenAI, agentic AI, and MLOps on Azure.
- Hybrid work, a learning-first culture, and support for Azure certifications.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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