The Role
Design, develop, and deploy LLM-powered applications using RAG and agentic AI. Build LangChain/LangGraph agents, scalable Python ML solutions (PyTorch, TensorFlow, Hugging Face), implement vector search (FAISS, Pinecone), fine-tune and evaluate LLMs, create REST APIs (FastAPI/Flask), deploy on cloud (AWS/Azure/GCP), and apply MLOps/LLMOps, CI/CD, monitoring, and governance.
Summary Generated by Built In
We are seeking highly experienced Technical Architects, Solution Architects, and AI Tech Leads with deep expertise in Python, Generative AI, Machine Learning, LLMs, RAG, and Agentic AI to help build next-generation AI-powered applications.
Key Responsibilities:
🔹 Design and implement LLM-powered applications using RAG
architectures
🔹 Build and orchestrate AI agents using LangChain and LangGraph
🔹 Develop scalable AI/ML solutions and semantic retrieval systems
🔹 Fine-tune, evaluate, and optimize LLM performance
🔹 Architect enterprise-grade AI platforms and deployment strategies
🔹 Establish MLOps/LLMOps best practices and governance frameworks
architectures
🔹 Build and orchestrate AI agents using LangChain and LangGraph
🔹 Develop scalable AI/ML solutions and semantic retrieval systems
🔹 Fine-tune, evaluate, and optimize LLM performance
🔹 Architect enterprise-grade AI platforms and deployment strategies
🔹 Establish MLOps/LLMOps best practices and governance frameworks
Required Skills:
✔️ 10+ years of Python Development & Machine Learning experience
✔️ 3+ years of hands-on experience with GenAI, LLMs, and RAG
architectures
✔️ Strong expertise in LangChain and/or LangGraph
✔️ Experience building AI Agents and Multi-Agent Systems
✔️ Expertise with Vector Databases, Semantic Search, and Retrieval Systems
✔️ Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-
learn, Pandas, NumPy
✔️ REST API development using FastAPI or Flask
✔️ Cloud deployment experience on AWS, Azure, or GCP
✔️ Strong knowledge of Docker, Kubernetes, Git, CI/CD, MLOps, and
LLMOps
✔️ Experience with Prompt Engineering, AI Evaluation, Monitoring, and
Governance
✔️ 3+ years of hands-on experience with GenAI, LLMs, and RAG
architectures
✔️ Strong expertise in LangChain and/or LangGraph
✔️ Experience building AI Agents and Multi-Agent Systems
✔️ Expertise with Vector Databases, Semantic Search, and Retrieval Systems
✔️ Hands-on experience with PyTorch, TensorFlow, Hugging Face, Scikit-
learn, Pandas, NumPy
✔️ REST API development using FastAPI or Flask
✔️ Cloud deployment experience on AWS, Azure, or GCP
✔️ Strong knowledge of Docker, Kubernetes, Git, CI/CD, MLOps, and
LLMOps
✔️ Experience with Prompt Engineering, AI Evaluation, Monitoring, and
Governance
Preferred Qualifications:
⭐ Experience with Azure OpenAI, LlamaIndex, LangSmith, or similar frameworks
⭐ Exposure to AI evaluation, monitoring, and guardrail frameworks
⭐ Contributions to open-source AI/ML projects
⭐ Experience integrating AI solutions with React-based applications
⭐ Exposure to AI evaluation, monitoring, and guardrail frameworks
⭐ Contributions to open-source AI/ML projects
⭐ Experience integrating AI solutions with React-based applications
Skills Required
- 5+ years of Python development and Machine Learning experience
- 2+ years hands-on experience with GenAI, RAG, and LLM applications
- Strong expertise in LangChain and/or LangGraph
- Experience developing scalable ML/AI solutions using PyTorch, TensorFlow, Hugging Face, scikit-learn, Pandas, NumPy
- Experience with vector databases, semantic search, and retrieval systems (e.g., FAISS, Pinecone)
- Knowledge of Agentic AI, prompt engineering, and workflow orchestration
- Experience building RESTful APIs using FastAPI or Flask
- Experience with Docker, Kubernetes, Git, and cloud deployment platforms (AWS, Azure, GCP)
- Implement MLOps/LLMOps best practices, CI/CD pipelines, monitoring, and governance controls
- Strong problem-solving and communication skills
- Experience with Azure OpenAI, LlamaIndex, LangSmith, or similar frameworks
- Exposure to AI evaluation, monitoring, and guardrail frameworks
- Contributions to open-source AI/ML projects
- Experience integrating AI solutions with React-based applications
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The Company
What We Do
We built Trackmind to be the partner we always wished we’d had. We got our start 10 years ago, designing both physical products and software for leading companies. Along the way, we’ve experienced every possible headache that comes with bringing products to market -- hiring agency after agency, constantly taking three steps forward and two steps back. Our services are intended to simplify and accelerate growth for ambitious companies at different stages in their journey
