Verified Job
IT / Software / Data Analyst
Artificial Intelligence Architect
IT / Software / Data Analyst
#746253
Remote / WFH
BRAINSEARCH CONSULTING PVT LTD · Bengaluru, Karnataka ·
Job Description
Job description
Job Description:Technical Core Responsibilities:1. Agent Architecture & Orchestration:- Design & Develop: Build sophisticated AI agents capable of autonomous reasoning, task planning, and execution.- Multi-Agent Coordination: Implement complex orchestration patterns using frameworks like LangGraph, AutoGen, or CrewAI to manage hand-offs and collaboration between specialized agents.- Tool Integration: Develop seamless "tool-use" (function calling) capabilities, allowing LLMs to interact with external APIs, databases, and legacy software.2. Prompt Engineering & Reliability:- Optimization: Create and refine advanced prompting strategies (Chain-of-Thought, ReAct, Reflexion) to improve agent reliability.- Structured Outputs: Ensure system stability by enforcing structured outputs (JSON, Pydantic) to bridge the gap between stochastic LLM responses and deterministic code.3. AI Operations (AIOps):- CI/CD for ML: Design and operate automated pipelines specifically for AI/ML systems, including versioning of prompts, models, and agent configurations.- Monitoring & Evaluation: Implement "LLM-as-a-judge" or traditional evaluation frameworks to monitor agent performance and drift in production.Required Technical Stack:- Languages: Expert-level Python (AsyncIO, Pydantic, FastAPI).- Frameworks: Deep experience with LangChain and at least one agentic framework (LangGraph is highly preferred for cyclic workflows).- LLMs: Hands-on experience with frontier models (OpenAI o1/GPT-4, Claude 3.5 Sonnet, Gemini 1.5 Pro) and their specific tool-calling nuances.- Infrastructure: Experience with Docker, Cloud providers (Azure/AWS/GCP), and modern CI/CD tools (GitHub Actions, GitLab CI). (ref:hirist.tech)
Job Description:Technical Core Responsibilities:1. Agent Architecture & Orchestration:- Design & Develop: Build sophisticated AI agents capable of autonomous reasoning, task planning, and execution.- Multi-Agent Coordination: Implement complex orchestration patterns using frameworks like LangGraph, AutoGen, or CrewAI to manage hand-offs and collaboration between specialized agents.- Tool Integration: Develop seamless "tool-use" (function calling) capabilities, allowing LLMs to interact with external APIs, databases, and legacy software.2. Prompt Engineering & Reliability:- Optimization: Create and refine advanced prompting strategies (Chain-of-Thought, ReAct, Reflexion) to improve agent reliability.- Structured Outputs: Ensure system stability by enforcing structured outputs (JSON, Pydantic) to bridge the gap between stochastic LLM responses and deterministic code.3. AI Operations (AIOps):- CI/CD for ML: Design and operate automated pipelines specifically for AI/ML systems, including versioning of prompts, models, and agent configurations.- Monitoring & Evaluation: Implement "LLM-as-a-judge" or traditional evaluation frameworks to monitor agent performance and drift in production.Required Technical Stack:- Languages: Expert-level Python (AsyncIO, Pydantic, FastAPI).- Frameworks: Deep experience with LangChain and at least one agentic framework (LangGraph is highly preferred for cyclic workflows).- LLMs: Hands-on experience with frontier models (OpenAI o1/GPT-4, Claude 3.5 Sonnet, Gemini 1.5 Pro) and their specific tool-calling nuances.- Infrastructure: Experience with Docker, Cloud providers (Azure/AWS/GCP), and modern CI/CD tools (GitHub Actions, GitLab CI). (ref:hirist.tech)
