Role overview
- The client is seeking a Full-Stack AI/ML Engineer to build and deploy AI-driven solutions that address complex business challenges from data to production.
- The role involves data acquisition, feature engineering, model development, LLM integration, deployment, and ongoing optimization.
- The engineer will collaborate closely with product owners, program managers, and cross-functional Scrum teams to deliver scalable, high-impact AI capabilities aligned with business goals.
What you'll work on
- Design and build AI/ML solutions that automate, optimize, or enhance business workflows.
- Acquire and preprocess structured/unstructured data from diverse sources (APIs, databases, OCR pipelines, documents, etc.).
- Conduct Exploratory Data Analysis (EDA) and develop statistical and predictive models using Python and ML frameworks.
- Build and fine-tune Large Language Model (LLM) pipelines (e.g., OpenAI, Azure OpenAI, Hugging Face, LangChain).
- Implement retrieval-augmented generation (RAG) and document-intelligence systems.
- Develop and deploy production-grade APIs and microservices using FastAPI or similar, integrated with MLOps practices.
- Collaborate with data engineers to ensure efficient data pipelines and with software engineers to integrate models into products.
- Continuously monitor, retrain, and optimize deployed models.
- Research and prototype emerging AI methods multimodal models and AI agents.
- Document architecture, design choices, and experiment outcomes for transparency and reproducibility.
- Work as a core member of a cross-functional AI team, contributing to sprint planning, backlog grooming, daily stand-ups, and retrospectives under Scrum / Agile frameworks.
- Participate in peer code reviews, ensure clean coding practices, and contribute to shared libraries and internal AI frameworks.
What we're looking for
- 5+ years of hands-on experience in data science, ML engineering, or applied AI with production deployments.
- Strong proficiency in Python (Pandas, NumPy, Scikit, LangChain, and LangGraph, etc.) and SQL.
- Experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
- Skilled in data acquisition, ETL pipelines, and feature engineering using APIs, cloud storage, or databases.
- Proficiency in FastAPI for serving models as microservices.
- Experience building and managing MLOps pipelines with tools like Docker, Kubernetes, and CI/CD.
- Hands-on experience with cloud platforms (Azure, AWS, or GCP) and their ML/AI services.
- Working knowledge of Large Language Models (LLMs) and Generative AI frameworks
- Strong understanding of EDA, model validation, and experiment tracking.
- Familiarity with vector databases for semantic retrieval or RAG pipelines.
- Comfortable working in Agile/Scrum teams participating in sprint planning, stand-ups, reviews, and retrospectives.
- Collaborative team player with excellent communication and documentation skills.
- Demonstrated ability to take AI/ML models from prototype to production and continuously improve them.
Tags & focus areas
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