AI/ML Engineer

Overview

We are seeking an experienced AI/ML Engineer to support the design, development, and deployment of scalable machine learning and generative AI solutions. This role will focus on building production‑ready models, optimizing data pipelines, and collaborating with cross‑functional teams to deliver high‑impact AI initiatives.

Key Responsibilities

• Design, develop, and deploy machine learning models and AI solutions in production environments
• Build and optimize LLM‑based applications (e.g., RAG pipelines, prompt engineering, agent workflows)
• Develop and maintain data pipelines to support model training, evaluation, and inference
• Implement MLOps best practices, including model versioning, monitoring, and automated retraining
• Collaborate with data engineers, product managers, and stakeholders to translate business requirements into technical solutions
• Evaluate model performance and continuously improve accuracy, scalability, and efficiency
• Ensure compliance with data governance, security, and responsible AI standards

Required Qualifications

• 6–7 years of experience in AI/ML engineering, data science, or related field
• Strong programming skills in Python (NumPy, Pandas, Scikit‑learn)
• Hands‑on experience with TensorFlow or PyTorch
• Experience building and deploying LLM‑based solutions (RAG, embeddings, vector databases)
• Solid understanding of data engineering concepts (ETL, data pipelines, distributed processing)
• Experience with cloud platforms (AWS, Azure, or Google Cloud)
• Familiarity with MLOps tools (e.g., MLflow, Airflow, Docker, CI/CD pipelines)
• Strong problem‑solving skills and ability to work independently in a fast‑paced environment

Preferred Qualifications

• Experience with Databricks, Snowflake, or similar data platforms
• Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate)
• Exposure to agentic AI frameworks (e.g., LangChain, LlamaIndex)
• Experience working in regulated environments (finance, healthcare, or government)
• Familiarity with AI governance frameworks (e.g., NIST AI RMF, Responsible AI practices)