AI Engineer - Python full-stack/backend engineering capability
Tavant Technologies · Noida · onsite · Posted 21d ago
Undisclosed5–10 yrsfulltimeSourced from
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About this role
Frontend: React / Next.js / Angular
Backend: Python + FastAPI + Pydantic
Database: PostgreSQL + Redis
APIs: REST APIs + external API/tool integrations
AI layer: LangChain + LangGraph + LLM APIs
RAG: Vector DB + embeddings + similarity search
Agent integration: MCP
Infrastructure: Docker + Kubernetes + Azure/AWS/GCP
Engineering: Git + Pytest + Ruff + package/environment management
Production: Security + observability + evaluation + performance + cost optimization
Backend: Python + FastAPI + Pydantic
Database: PostgreSQL + Redis
APIs: REST APIs + external API/tool integrations
AI layer: LangChain + LangGraph + LLM APIs
RAG: Vector DB + embeddings + similarity search
Agent integration: MCP
Infrastructure: Docker + Kubernetes + Azure/AWS/GCP
Engineering: Git + Pytest + Ruff + package/environment management
Production: Security + observability + evaluation + performance + cost optimization
Willing to work atleaset 4 hours in PST
AI Engineer, Agentic Systems
Location: Remote, PST hours preferred
Department: Engineering
Reports to: Ovais Malik
Employment Type: Contract/Hourly
Department: Engineering
Reports to: Ovais Malik
Employment Type: Contract/Hourly
About the Role
Trusaic is looking for an AI Engineer to build production LLM applications and autonomous agents using Python, LangChain, and LangGraph. You will work across the full lifecycle, from early prototypes to deployed systems our customers rely on. We do not expect candidates to match every tool in the broader AI ecosystem, but we do expect strength in the primary skills below.
Trusaic is looking for an AI Engineer to build production LLM applications and autonomous agents using Python, LangChain, and LangGraph. You will work across the full lifecycle, from early prototypes to deployed systems our customers rely on. We do not expect candidates to match every tool in the broader AI ecosystem, but we do expect strength in the primary skills below.
Primary Skills (Must Have)
Python, with a track record of writing clean, tested production code. LangChain for building LLM applications and agents that use tools. LangGraph for building workflows that coordinate multiple agents.
Python, with a track record of writing clean, tested production code. LangChain for building LLM applications and agents that use tools. LangGraph for building workflows that coordinate multiple agents.
Responsibilities
Build LLM applications and agent workflows using LangChain and LangGraph. Write clean, tested, maintainable Python code. Move prototypes out of notebooks and into production, typically served through FastAPI. Integrate RAG, vector search, and external tools into agent workflows using protocols like MCP. Work with product and engineering teams to turn requirements into working system(s). Monitor and improve how models and agents perform in production, including evaluation, observability, guardrails, security, compliance, cost, and performance.
Build LLM applications and agent workflows using LangChain and LangGraph. Write clean, tested, maintainable Python code. Move prototypes out of notebooks and into production, typically served through FastAPI. Integrate RAG, vector search, and external tools into agent workflows using protocols like MCP. Work with product and engineering teams to turn requirements into working system(s). Monitor and improve how models and agents perform in production, including evaluation, observability, guardrails, security, compliance, cost, and performance.
Required Qualifications
Minimum 5 years of Python engineering experience. Hands on experience with LangChain and/or LangGraph, whether professional or personal. Strong fundamentals in testing, version control, and API design. Experience working with LLM provider APIs. Experience deploying on a major cloud platform. Azure is preferred, GCP or AWS also acceptable. Experience building or serving applications with FastAPI. Experience using MCP for tool integration. Experience with vector databases for similarity search. Experience with LLM evaluation and observability tooling. Experience applying guardrails, security, and compliance practices to AI systems, along with attention to cost and performance.
Minimum 5 years of Python engineering experience. Hands on experience with LangChain and/or LangGraph, whether professional or personal. Strong fundamentals in testing, version control, and API design. Experience working with LLM provider APIs. Experience deploying on a major cloud platform. Azure is preferred, GCP or AWS also acceptable. Experience building or serving applications with FastAPI. Experience using MCP for tool integration. Experience with vector databases for similarity search. Experience with LLM evaluation and observability tooling. Experience applying guardrails, security, and compliance practices to AI systems, along with attention to cost and performance.
Core Python
- Python 3.11/3.12+ primary language
- uv fast Python package/project management
- venv built-in virtual environments
- Poetry alternative dependency/project management
- Ruff linting + formatting
- Pytest testing
- Jupyter experimentation and notebooks
Machine Learning
- NumPy numerical computing
- pandas / Polars data manipulation
- scikit-learn classical ML
- XGBoost / LightGBM gradient boosting
- SciPy scientific computing
Deep Learning
- PyTorch most important general-purpose deep-learning framework
- TensorFlow / Keras alternative deep-learning ecosystem
Generative AI / LLMs
- Hugging Face Models / Datasets for different kinds of PoC
- TRL LLM post-training (good to have)
- sentence-transformers embeddings
- tokenizers fast tokenization
LLM Applications / Agents
- LangChain LLM application/agent framework
- LlamaIndex RAG and data integration
- PydanticAI typed Python AI/agent applications
- DSPy programmatic LLM pipelines
- LiteLLM unified interface to multiple LLM APIs
RAG / Vector Search
A typical stack is:
Python tools:
- FAISS
- Qdrant
- Chroma
- Weaviate
- Milvus
- pgvector + PostgreSQL
AI APIs Deployment
- FastAPI Python API backend
- Pydantic validation/data models
- Uvicorn ASGI server
- Docker containerization
- Kubernetes orchestration
- MLflow experiment/model tracking
- Weights Biases experiment tracking
- ONNX / ONNX Runtime model deployment
Data Infrastructure
- PostgreSQL
- Redis
- Apache Kafka
- Apache Spark
- DuckDB
- Polars
- Parquet
AI Application UI
- Streamlit fastest way to prototype AI apps
- Gradio excellent for ML demos
- FastAPI + React/Next.js / Angular 19+ production application
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Sourced from naukri
Skills
PostgresqlPrototypeEnvironment managementgoogle cloud platformBackendPythonProject ManagementDeep LearningGITMachine Learning
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