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AI & Data Engineering Manager (Lead Member of Technical Staff- LMTS)

Position Summary

This position requires expertise in designing, developing, debugging, and maintaining AI-powered applications and data engineering workflows for both local and cloud environments. The role involves working on large-scale projects, optimizing AI/ML pipelines, and ensuring scalable data infrastructure.

As a LMTS, you will be responsible for building Generative AI (GenAI) capabilities, building data pipelines for AI model training, and deploying scalable AI-powered microservices. You will collaborate with other AI/ML, Data Engineering, DevOps, and Product teams to deliver impactful solutions that enhance our products and services.

Preferred qualifications include experience with retrieval-augmented generation (RAG), fine-tuning pre-trained LLMs, AI model evaluation, data pipeline automation, and optimizing cloud-based AI deployments.

Responsibilities

AI-Powered Software Development & API Integration

• Develop AI-driven applications, microservices, and automation. Build and maintain Python based AI services using lang Chain and CrewAI.
• Integrate and optimize OpenAI APIs (GPT models, Embeddings, Function Calling), hugging Face, LangChain, and implement Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval and classification, agentic AI workflows
• Deploy AI-powered applications using AWS Lambda, Kubernetes, Docker, CI/CD pipelines

Data Engineering & AI Model Performance Optimization

• Experience with building and optimizing scalable data pipelines for AI/ML workflows using Pandas, PySpark, and Dask, integrating data sources such as Kafka, AWS S3, Azure Data Lake, and Snowflake.
• Experience in assessing AI models using model scoring, fine tuning embeddings and enhance similarity search for retrieval augmented applications
• Experience on enhancing AI model inference efficiency by implementing vector retrieval using FAISS, Pinecone, or ChromaDB, and optimize API latency with tuning techniques (temperature, top-k sampling, max tokens settings).

Microservices, APIs & Security

• Design and develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT authetication, and API rate limiting.
• Implement AI-powered logging, observability, and monitoring to track data pipelines, model drift, and inference accuracy, ensuring compliance with AI governance and security best practices.

AI & Data Engineering Collaboration

• Work with AI/ML, Data Engineering, and DevOps teams to optimize AI model deployments, data pipelines, and real-time/batch processing for AI-driven solutions.
• Engage in Agile ceremonies, backlog refinement, estimate task accurately, drive incremental release of AI features, brainstorm different technical approaches to provide solutions in areas like fraud detection, claims processing, and intelligent automation.

Cross-Functional Coordination and Communication

• Work collaboratively across Technology, Product, AI/ML, and DevOps teams to align AI-driven enhancements with business goals.
• Build strong relationships with AI engineers, data scientists, and cloud architects to optimize LLM-based applications.
• Ensure AI compliance with security, ethical AI policies, and privacy standards (HIPAA, GDPR, SOC2, AI governance best practices).

Mentorship & Knowledge Sharing

• Mentor junior engineers on AI model integration, API development, and scalable data engineering best practices, and conduct knowledge-sharing sessions.

Education & Experience Required

• 10-14 years of experience in software engineering or AI/ML development, preferably in AI-driven solutions.
• Hands-on experience with Agile development, SDLC, CI/CD pipelines, and AI model deployment lifecycles.
• Bachelor’s Degree or equivalent in Computer Science, Engineering, Data Science, or a related field.
• Proficiency in full-stack development with expertise in Python (preferred for AI)
• Experience with structured & unstructured data: SQL, NoSQL, Big Data, Vector Databases and AI data pipelines
• Familiarity with Cloud & AI Infrastructure such as AWS, Azure, Kubernetes, Docker, CI/CD
• Experience with GenAI Frameworks & Tools: OpenAI API, Hugging Face Transformers, TensorFlow, LangChain, LlamaIndex, CrewAI.
• Experience in LLM deployment, retrieval-augmented generation (RAG), and AI search optimization.
• Experience in AI model evaluation (BLEU, ROUGE, BERT Score, cosine similarity) and responsible AI deployment.
• Strong problem-solving skills, AI ethics awareness, and the ability to collaborate across AI, DevOps, and data engineering teams.
• Curiosity and eagerness to explore new AI models, tools, and best practices for scalable GenAI adoption.