AI/ML Engineer

Job Description: Key Responsibilities Lead activities across the AI/ML lifecycle – from ideation, research, data engineering, model development and optimization, evaluation, performance tuning and deployment, while continuously engaging customers to gather feedback and incorporate it into solution development. Iteratively develop, deploy and scale AI/ML models and solutions across life sciences, genomics, material sciences, and healthcare. Contribute to the development of AI/ML models and solutions, following established model and system architectures, software design standards, reusable patterns, and best practices for AI and Generative AI solutions. Apply evaluation-driven approaches to AI/ML development by implementing and running evaluation frameworks, analyzing model performance, and using results to improve the quality and reliability of AI/ML solutions. Build and deploy LLM-powered services using Azure OpenAI, Anthropic Claude, and OpenAI-compatible APIs. Architect and implement agentic AI and RAG workflows, including data ingestion, chunking, embeddings, vector search, retrieval, tool calling, memory, and prompt engineering. Design, develop, and integrate Generative AI systems using LangChain and LangGraph for agentic workflows and orchestration. Integrate AI/Generative AI capabilities into enterprise platforms, scientific applications and end-to-end workflows. Mentor and guide engineers across the AI/ML lifecycle, including model development, evaluation, and implementation of AI solutions. Actively participate in Communities of Practice, influencing engineering standards and AI/Generative AI adoption strategies across the organization. Communicate effectively with technical and non-technical stakeholders through clear documentation, architecture diagrams and design reviews. Stay current with advancements in AI/ML, Generative AI, agentic frameworks, and LLM ecosystems, and apply relevant innovations to enhance internal tools, scientific solutions and customer-facing products. Candidate Requirement: Education and Experience: Bachelor’s degree in AI/ML, computer science, statistics, engineering, or a related technical field. Master’s degree preferred. 6+ years of industry experience in software engineering and developing AI/ML solutions, with a strong track record of shipping these into real production systems in a robust experimentation framework, not just offline analyses or research prototypes. 4+ years of experience working in agile/scrum environments. Hands-on experience in developing and applying AI techniques and algorithms, including deep learning, CNNs, decision trees, clustering, ensembles, and related approaches. Hands-on experience developing retrieval-augmented generation (RAG) and agentic AI solutions, including embeddings, retrieval, vector search, tool calling, prompt engineering, orchestration, and evaluation. Strong proficiency in Python, PyTorch, C++, C#, and other relevant programming languages and frameworks. Expertise with backend engineering best practices, with demonstrated ability to design, build and own reliable, scalable systems that serve users. Experience with LangChain and LangGraph for LLM orchestration and agentic workflows. Strong data engineering skills, including ETL/data pipelines and large-scale data processing and analysis using tools such as Pandas and NumPy. Ability to work closely with backend, platform, and application engineers on model serving, pipeline architecture, deployment infrastructure, and production integration with sound judgement in balancing scope, quality, and speed to delivery. Demonstrated experience leveraging AI coding assistants or agents as part of your engineering workflow. Excellent written and verbal communication skills, with the ability to explain complex technical concepts clearly. Flexibility and adaptability to work in a fast-paced and collaborative environment. Preferred: Hands-on experience developing and deploying AI/ML models and solutions for life sciences, genomics, materials sciences, healthcare, or other regulatory settings. Preferred: Experience with MLOps or LLMOps concepts including deployment, monitoring, orchestration, observability, and model lifecycle management. Nice-to-have: Experience applying AI/ML models and methods to computational biology. Nice-to-have: Experience with cloud platforms such as Azure, AWS or GCP.