AI/ML Engineer

Hybrid Details:
4 days/week onsite required

Duration:
6 months to start (with potential for extension or conversion)

Job Description

• 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.

Job Responsibilities

• 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.

Requirements

• 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.