Tech Lead

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