Associate Technical Lead – Data Science

About this role

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Skills you'll use

What you'll do

• Drive end-to-end AI application development—from idea conception and hypothesis formulation through rigorous testing to production deployment and monitoring, ensuring solutions solve real-world problems.
• Embrace a boundaryless approach to technology, collaborating across backend systems, frontend interfaces (where applicable), automation scripts, and data pipelines to deliver holistic solutions.
• Design, implement, and meticulously fine-tune LLM prompts, embeddings, RAG pipelines, and sophisticated AI agents (using frameworks like Langchain, CrewAI, Bedrock Agents, OpenAI SDK, Autogen etc.) tailored to specific domain use cases and business objectives.
• Architect and build Generative AI solutions that are inherently scalable, secure, cost-efficient, and privacy-conscious, integrating these considerations from the initial design phase.
• Implement robust testing methodologies and MLOps practices specifically for GenAI, focusing on preventing prompt drift, mitigating hallucinations, ensuring model regression testing, and maintaining high performance.
• Maintain exceptionally high standards in code hygiene, implement robust CI/CD practices for AI models and applications, and produce clear, comprehensive documentation.
• Collaborate closely with data analysts and engineers to guarantee data quality, readiness, and the efficiency of data pipelines feeding into GenAI models and applications.
• Formulate and validate hypotheses by engaging directly with stakeholders, understanding their needs deeply, and translating them into actionable technical requirements.
• Champion rapid prototyping and lean experimentation methodologies: build fast, fail fast, learn faster, and iterate quickly to test ideas before committing significant resources.
• Engage proactively and cross-functionally with Product Management, Design, Business Analysts, Security, and Legal teams to navigate complex requirements and deliver impactful solutions.

Who can apply

• A minimum of 6+ years of Data Science experience, demonstrating strong expertise in building robust AI/ML systems and scalable application architectures.
• At least 2+ years of focused, hands-on experience building and deploying practical Generative AI applications, such as AI virtual assistants, complex AI agents, and content generation tools.
• Managed and delivered solutions for X+ clients, contributing to ₹Y+ in revenue and ensuring high client satisfaction across multiple verticals.
• Spearheaded initiatives that improved operational efficiency and scalability, leading to measurable gains in revenue growth and client acquisition.
• Proven experience utilizing core GenAI tools and platforms like AWS Bedrock (including various foundation models and Bedrock Agents), LangChain, designing and implementing RAG pipelines, leveraging Agent-based frameworks (e.g., CrewAI), working with vector databases (e.g., Pinecone, Weaviate, Qdrant, Milvus, ChromaDB), and understanding LLM principles.
• Experience with model training/fine-tuning (e.g., LoRA/QLoRA) and core NLP concepts is essential.
• High proficiency in Python and strong familiarity with modern ML/AI libraries (e.g., Hugging Face Transformers, PyTorch/TensorFlow, Scikit-learn, Pandas, NumPy) and relevant SDKs (e.g., Boto3, OpenAI SDK).
• Demonstrated practical experience in developing, performance tuning, and optimizing LLM-based applications, including sophisticated prompt engineering techniques.
• Experience leveraging AI coding assistants (e.g., GitHub Copilot, Amazon Q, Cursor) to accelerate development workflows while maintaining code quality.
• Deep understanding and practical application of data privacy principles, security best practices, and cost-aware system design, specifically within the context of AI and Generative AI applications.

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