Lead Engineer/Associate Architect - Data/AI

Description:

Responsibilities:

• Architect and Design:

  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

• AI/ML Development:

  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)

• Technical Leadership:

  Act as a technical lead, mentoring junior engineers, setting best practices, and performing code reviews to ensure high-quality and efficient code

• Strategic Consultation:

  Work directly with our client's technical and product teams to understand requirements and translate them into a clear technical vision and roadmap

• Innovation:

  Stay current with the latest advancements in AI, LLMs, machine learning, and data engineering to introduce new technologies and methodologies that improve our capabilities

• Collaboration:

  Partner with cross-functional teams, including product managers, data scientists, and software engineers, to deliver integrated solutions

Requirements

• Extensive Experience:

  8+ years of hands-on experience in AI/ML and data engineering

• Scalable AI Systems:

  Proven experience designing and implementing

  scalable AI systems

  in a production environment. This includes a deep understanding of MLOps principles and tools

• Technical Expertise:

  • Data Technologies:

    Proficiency with modern data platforms and tools (e.g., Databricks, Snowflake, Apache Spark, Flink, Airflow, Kafka)

  • AI/ML Frameworks:

    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

  • Gen AI Frameworks: Exposure to LLMs like Open AI, Claude, Gemini etc and frameworks like LangChain, LangGraph,LangSmith, LangFlow etc

  • Programming Languages:

    Expert-level proficiency in at least one key programming language (e.g., Python, Scala, Java)

• Cloud Platforms:

  Extensive experience with one or more major cloud providers (AWS, GCP, or Azure), including their data and AI services

• Data Engineering

  : Exposure to big data processing using PySpark or Databricks is an added advantage while not a mandatory skill

• Problem-Solving:

  Exceptional analytical and problem-solving skills, with the ability to tackle complex, ambiguous challenges independently

• Communication:

  Excellent communication and presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders

Preferred Qualifications:

• Experience with a diverse set of AI applications, such as computer vision, natural language processing, or time-series analysis
• Background in an industry with complex data requirements (e.g., finance, e-commerce, healthcare)
• A portfolio of open-source contributions demonstrating real-world projects and technical depth