Lead development of LLM-powered applications and agentic workflows: implement RAG pipelines, vector DB integrations, prompt orchestration, AI agents, and REST microservices. Optimize inference, deployment (containers/CI-CD/cloud), and evaluate model accuracy/hallucination. Debug performance issues and document designs.
Responsibilities
GenAI Application Development
- Develop LLM-powered applications using Python-based frameworks.
- Implement RAG pipelines and vector database integrations.
- Build prompt orchestration workflows and response optimization logic.
Agentic Workflow Implementation
- Develop AI agents with tool integration capabilities.
- Implement reasoning loops, memory modules, and execution controls.
- Integrate AI agents into backend systems and APIs.
Deployment & Optimization
- Build REST APIs and microservices for AI applications.
- Optimize inference performance, latency, and cost.
- Support CI/CD pipelines and cloud deployments.
Testing & Quality
- Implement evaluation metrics for hallucination control and accuracy.
- Debug and resolve performance bottlenecks.
- Document code, workflows, and solution design.
Experience and Competency Requirements
- 4–8 years of software development experience.
- 2+ years working with AI/ML or GenAI applications.
- Strong proficiency in Python.
- Experience with LLM APIs, embeddings, and vector databases.
- Exposure to cloud-based deployments and containerization.
- Strong analytical and debugging capabilities.
- Should have decent to good experience in data handling and analytics with python
Skills
GenAI & LLM Frameworks (Mandatory)
- OpenAI APIs / Azure OpenAI
- LangChain / LangGraph / LlamaIndex
- Transformers (Hugging Face)
- Prompt engineering and evaluation frameworks
Agentic Systems & Orchestration
- Multi-agent design patterns (MCP, A2A, ReAct etc)
- Tool integrations and API orchestration
- Memory frameworks and contextual reasoning
- Guardrails, observability, and monitoring
Data & Infrastructure
- Vector databases (Pinecone, FAISS, Weaviate or equivalent)
- Python, FastAPI, REST services
- Docker, Kubernetes
- Cloud platforms (AWS, Azure, GCP)
Data Handling & Analytics Skills
- Data preprocessing and ETL for structured and unstructured data
- Data manipulation using Pandas, NumPy, and SQL
- Exploratory data analysis (EDA) and statistical analysis
- Data visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI)
Responsibilities
GenAI Application Development
- Develop LLM-powered applications using Python-based frameworks.
- Implement RAG pipelines and vector database integrations.
- Build prompt orchestration workflows and response optimization logic.
Agentic Workflow Implementation
- Develop AI agents with tool integration capabilities.
- Implement reasoning loops, memory modules, and execution controls.
- Integrate AI agents into backend systems and APIs.
Deployment & Optimization
- Build REST APIs and microservices for AI applications.
- Optimize inference performance, latency, and cost.
- Support CI/CD pipelines and cloud deployments.
Testing & Quality
- Implement evaluation metrics for hallucination control and accuracy.
- Debug and resolve performance bottlenecks.
- Document code, workflows, and solution design.
Experience and Competency Requirements
- 4–8 years of software development experience.
- 2+ years working with AI/ML or GenAI applications.
- Strong proficiency in Python.
- Experience with LLM APIs, embeddings, and vector databases.
- Exposure to cloud-based deployments and containerization.
- Strong analytical and debugging capabilities.
- Should have decent to good experience in data handling and analytics with python
Skills
GenAI & LLM Frameworks (Mandatory)
- OpenAI APIs / Azure OpenAI
- LangChain / LangGraph / LlamaIndex
- Transformers (Hugging Face)
- Prompt engineering and evaluation frameworks
Agentic Systems & Orchestration
- Multi-agent design patterns (MCP, A2A, ReAct etc)
- Tool integrations and API orchestration
- Memory frameworks and contextual reasoning
- Guardrails, observability, and monitoring
Data & Infrastructure
- Vector databases (Pinecone, FAISS, Weaviate or equivalent)
- Python, FastAPI, REST services
- Docker, Kubernetes
- Cloud platforms (AWS, Azure, GCP)
Data Handling & Analytics Skills
- Data preprocessing and ETL for structured and unstructured data
- Data manipulation using Pandas, NumPy, and SQL
- Exploratory data analysis (EDA) and statistical analysis
- Data visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI)
Minimum Qualification
Bachelor’s degree required
M.Tech/ MS in Computer Science, AI, or related field preferred; Required Experience: 4-6 years
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