Role: Agentic AI Architect
Location: Remote, US
Mode: Remote
Type: Contract
Job Description:
Key Responsibilities:
- Architect and implement agentic AI systems using modern LLM orchestration frameworks (LangChain, CrewAI, AutoGen, etc.).
- Design multi-agent collaboration models including planner-solver, autonomous teams, and goal decomposition agents.
- Build reusable tooling, APIs, and memory architectures for agent interaction, coordination, and context persistence.
- Lead hands-on development and deployment of GenAI applications (e.g., assistants, copilots, decision support).
- Evaluate and integrate LLMs (OpenAI, Claude, Mistral, LLaMA, etc.), vector databases (Pinecone, Weaviate, FAISS), and retrieval systems (RAG).
- Optimize agent performance for real-time environments, reliability, scalability, and ethical constraints.
- Guide teams in adopting agent frameworks, best practices, prompt engineering, and model fine-tuning.
- Collaborate with stakeholders to translate business requirements into technical solutions using agent-based paradigms.
- Continuously monitor trends in multi-agent systems, cognitive architectures, and open-source AI frameworks.
Must-Have Skills:
- 2+ years of hands-on experience in agentic AI / multi-agent systems.
- Proficiency with LangChain, Langgraph, CrewAI, AutoGen, Haystack, or equivalent frameworks.
- Strong background in Python and experience with prompt engineering, tools integration, and chaining logic.
- Solid understanding of LLM APIs, RAG, vector stores, tool use, and memory architectures.
- Hands-on experience with open-source and commercial LLMs (e.g., GPT-4, Claude, Gemini, Mistral).
- Experience deploying AI agents in cloud-native environments (AWS, GCP, Azure).
- Ability to lead architectural discussions, PoCs, and hands-on development in fast-paced environments.
- Model-cost profiling and budgeting (API call minimization, batch vs. streaming)
- Latency tuning for real-time agents, Autoscaling strategies.
Good-to-Have Skills:
- Exposure to Autonomous AI agents (AutoGPT, BabyAGI, CAMEL, MetaGPT).
- Understanding of LLM fine-tuning, adapters, and RLHF.
- Experience with agent simulation, environment modelling, or reinforcement learning is a plus.
- Familiarity with compliance, privacy, and safety in GenAI deployments.
- Prior experience in building domain-specific agents (Lifescience, healthcare, Pharma).
Job Type: Full-time
Pay: $100.00 - $120.00 per hour
Benefits:
Application Question(s):
- 1099-based candidates OR Citizen
Experience:
- Gen AI: 2 years (Required)
- LLM: 2 years (Required)
- LLM orchestration frameworks: 4 years (Required)
- Total: 10 years (Required)
- RAG, vector stores: 3 years (Required)
- Langchain: 3 years (Required)
- Multi Agent Systems: 4 years (Required)
- Python: 3 years (Required)
- Pharma Domain: 2 years (Preferred)
Work Location: Remote
