Job
Description
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.
