Tech Lead

Key Responsibilities:  Backend Development : Design, develop, and maintain REST APIs using Python to integrate AI/ML services. Agentic AI Development : Design and orchestrate AI agents capable of reasoning, planning, and executing multi step tasks by integrating LLMs with APIs, tools, and data sources. AI/ML Orchestration : Implement and manage machine learning models, large language models (LLMs), Agentic AI workflows and AI orchestration using Python. Generative AI Solutions : Using Generative AI models for tasks like text generation, summarization, conversational AI, and content creation. Data Management : Work with structured databases (SQL), graph databases (e.g., CosmosDB), and unstructured data stores (e.g., Elasticsearch). RAG Implementation : Build retrieval augmented generation (RAG) pipelines leveraging Azure AI Search, AWS OpenSearch, or other vector databases for contextual responses. Data Pipelines : Design and manage robust data ingestion and transformation pipelines to feed AI models. Intent Detection NLU : Develop or integrate intent detection models and natural language understanding (NLU) solutions to enhance conversational AI. Prompt Engineering Optimization : Create and optimize prompts for LLMs to improve response quality and reduce latency. AI Integration : Collaborate with frontend and product teams to embed Gen AI features into enterprise applications.  Required Skills:  Backend Development : Proficient in Python for building and maintaining scalable REST APIs, familiarity with integrating AI services. AI/ML Orchestration : Strong expertise in Python with a focus on machine learning, large language models (LLMs), AI orchestration. Agentic AI Expertise: Experience in building autonomous AI agents using frameworks like OpenAI Functions, or custom orchestration solutions to handle tool use and multi step reasoning. Generative AI Expertise : Generative AI models (text generation, summarization, conversational AI) and applying prompt engineering techniques. Data Management : Solid understanding of structured (SQL), graph (CosmosDB), and unstructured (Elasticsearch) databases; ability to design efficient data access patterns for AI workloads. RAG Implementation : Proven experience implementing retrieval augmented generation using Azure AI Search, AWS OpenSearch, or other vector databases. Data Pipelines : Hands on experience building and managing data ingestion and transformation pipelines using Databricks, Azure Data Factory, or equivalent tools. Intent Detection NLU : Skilled in developing or deploying intent detection models and natural language understanding (NLU) components for conversational AI applications.   Preferred Skills:   Familiarity with cloud platforms such as Azure and AWS. Knowledge of additional AI/ML frameworks and tools. Knowledge of Agentic AI Experience with DevOps practices and CI/CD pipelines