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Responsibilities

• Lead the architecture, design, and implementation of highly scalable and reliable data platforms and AI systems. This includes building foundational data pipelines, data lakes, and real-time data streams
• Drive the end-to-end lifecycle of machine learning models, from experimentation and feature engineering to deployment, monitoring, and maintenance in a production environment (MLOps)
• Act as a technical lead, mentoring junior engineers, setting best practices, and performing code reviews to ensure high-quality and efficient code
• Work directly with our client’s technical and product teams to understand requirements and translate them into a clear technical vision and roadmap
• Stay current with the latest advancements in AI, LLMs, machine learning, and data engineering to introduce new technologies and methodologies that improve our capabilities
• Partner with cross-functional teams, including product managers, data scientists, and software engineers, to deliver integrated solutions

Requirements

• 8+ years of hands-on experience in AI/ML and data engineering
• Proven experience designing and implementing scalable AI systems in a production environment. This includes a deep understanding of MLOps principles and tools
• Proficiency with modern data platforms and tools (e.g., Databricks, Snowflake, Apache Spark, Flink, Airflow, Kafka)
• Strong command of at least one major ML framework (e.g., TensorFlow, PyTorch). Experience with large language models (LLMs) and other generative AI technologies is a significant plus
• Exposure to LLMs like Open AI, Claude, Gemini etc and frameworks like LangChain, LangGraph, LangSmith, LangFlow etc
• Expert-level proficiency in at least one key programming language (e.g., Python, Scala, Java)
• Extensive experience with one or more major cloud providers (AWS, GCP, or Azure), including their data and AI services
• Exposure to big data processing using PySpark or Databricks is an added advantage while not a mandatory skill
• Exceptional analytical and problem-solving skills, with the ability to tackle complex, ambiguous challenges independently
• Excellent communication and presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.