Technical Lead (Enterprise AI Systems)

Job Description

Job description Technical Lead (Enterprise AI Systems) We are seeking a Technical Lead to drive the design and development of our enterprise-grade AI product. This role requires a deep understanding of AI/ML systems, cloud-native architectures, and scalable software design. The applicant should have mandatorily worked on enterprise grade AI product or project for at least 2 - 3 years. This role includes at least 50% hands on development, 30% LLD and the remaining 20% of HLD / Architecture. The role is on the technical career path and is NOT suitable for project managers, technical managers, engineering managers etc. Key Responsibilities: Develop scalable, modular, and secure AI-driven software systems. Design APIs, microservices, and cloud infrastructure for AI workloads. Ensure seamless AI model integration and efficient scaling. Optimize performance & scalability of systems. Implement caching, load balancing, and distributed computing. Ensure security compliance (GDPR, SOC2) and secure data handling. Enforce best practices in authentication, encryption, and API security. Mandatory Skills & Qualifications: Experience: 8 - 10 years in software architecture & design, with at least 3+ years focused on AI/ML product design & development. Last 1 - 2 years should include deep Gen AI experience in enterprise grade AI projects or applications. Experience with design and developing applications with LLMs, RAG Applications, Gen AI Agents. Fine-tuning open-source LLMs. Experience with deploying and monitoring LLMs. Experience with LLM evaluation frameworks or tools like DeepEval, Langfuse or similar. Experience with frameworks like langchain, llamaindex, pydantic etc. Proficiency in RDBMS like PostgreSQL and knowledge of vector databases (eg: chromadb,Pinecone, FAISS, Weaviate). Strong background in microservices, RESTful & GraphQL APIs, event-driven architectures, Working experience with Kafka, RabbitMQ etc... Proficient in Python, Java for backend development Experience with TensorFlow, PyTorch, Hugging Face etc... Skilled in data processing (NumPy, Pandas, Dask,NLTK) and front-end (TypeScript, React.js). Experience in scalable AI systems, data pipelines, MLOps, and real-time inference. Strong understanding of security protocols, IAM, OAuth2, JWT, RBAC. Expertise in cloud platforms (AWS, GCP, Azure), Kubernetes, Docker. Hands-on experience with CI/CD (Eg: github,Jenkins etc...) Hands-on with system monitoring (Eg: ELK Stack,OpenTelemetry, Prometheus, Grafana). Nice-to-Have Skills: Ensuring AI model interpretability, fairness, and bias mitigation strategies. Optimizing model inference using quantization, distillation, and pruning techniques. Experience in deploying AI models in production at scale, including MLOps best practices. Designing AI systems with privacy-preserving techniques (differential privacy, homomorphic encryption etc.). Experience with knowledge graph-based AI applications.