Principal Member of technical Staff- AI & Data Engineering

Responsibilities

• AI-Powered Software Development & API Integration
• Develop AI-driven applications, microservices, and automation workflows using FastAPI, Flask, or Django, ensuring cloud-native deployment and performance optimization.
• Integrate OpenAI APIs (GPT models, Embeddings, Function Calling) and Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval, classification, and decision-making.

Data Engineering & AI Model Performance Optimization

• Design, build, and optimize 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.
• Enhance 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

• Develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT, and API Key Authentication.
• 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, and collaborative problem-solving to scale AI-powered workflows in areas like fraud detection, claims processing, and intelligent automation.

Cross-Functional Coordination and Communication

• Collaborate with Product, UX, and Compliance teams to align AI-powered features with user needs, security policies, and regulatory frameworks (HIPAA, GDPR, SOC2).
• Ensure seamless integration of structured and unstructured data sources (SQL, NoSQL, vector databases) to improve AI model accuracy and retrieval efficiency.

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

• 12-18 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), Java
• Experience with structured & unstructured data:
• SQL (PostgreSQL, MySQL, SQL Server)
• NoSQL (OpenSearch, Redis, Elasticsearch)
• Vector Databases (FAISS, Pinecone, ChromaDB)

Cloud & AI Infrastructure:

• AWS: Lambda, SageMaker, ECS, S3
• Azure: Azure OpenAI, ML Studio
• GenAI Frameworks & Tools: OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, AutoGPT, CrewAI.
• Experience in LLM deployment, retrieval-augmented generation (RAG), and AI search optimization.
• Proficiency 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.