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EXL

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Project Manager

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Summary

Build and deploy GenAI applications using Python, LLM frameworks, and vector databases, then optimize and integrate them into cloud-based microservices.

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

Skills

Apply

See also

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