Senior Gen AI Engineer

Job Title:

Location

Experience

Job Type

Reports To

Job Overview

Key Responsibilities

• Agentic AI & Workflow Design

  : Lead design and implementation of Agentic AI systems and multi-step AI workflows. Build AI orchestration systems using frameworks like LangGraph. Utilize Agents, Tools, and Chains for complex task automation. Implement Agent-to-Agent (A2A) communication and Model Connect Protocol (MCP) for inter-model interactions.

• Production MLOps & Deployment

  : Develop, train, and deploy ML models optimized for production. Implement CI/CD pipelines (GitHub), automated testing, and robust observability (monitoring, logging, tracing) for Gen AI solutions. Containerize models (Docker) and deploy on cloud (AWS/Azure/GCP) using Kubernetes. Implement robust AI/LLM security measures and adhere to Responsible AI principles.

• AI Model Integration

  : Integrate LLMs and models from HuggingFace. Apply deep learning concepts with PyTorch or TensorFlow.

• Data & Prompt Engineering

  : Build scalable data pipelines for unstructured/text data. Design and implement embedding/chunking strategies for scalable data processing. Optimize storage/retrieval for embeddings (e.g., Pinecone, Weaviate). Utilize Prompt Engineering techniques to fine-tune AI model performance.

• Solution Development

  : Develop GenAI-driven Text-to-SQL solutions.

• Collaboration & Best Practices

  : Mentor junior team members on ML engineering best practices. Apply Agile/Scrum methodologies for project delivery.

Required Skills & Qualifications

• Education

  : BS/BTech or Master's degree in Computer Science, Data Science, AI, or ML (or equivalent experience).

• Experience

  : 6+ years in ML + data engineering , with 2+ years in LLM/GenAI projects. 3+ years developing machine learning models.

• Technical Skills

  :

• Programming

  : Python.

• Foundation Model APIs

  : AzureOpenAI, OpenAI, Gemini, Anthropic, or AWS Bedrock.

• Agentic AI & LLM Frameworks

  : LangChain, LangGraph, A2A, MCP, Chains, Tools, Agents. Ability to design multi-agent systems, autonomous reasoning pipelines, and tool-calling capabilities for AI agents.

• MLOps/LLMOps

  : Docker , Kubernetes (K8s) , CI/CD , Automated Testing, Monitoring, Observability, Model Registries, Data Versioning.

• Cloud Platforms

  : AWS/Azure/GCP.

• Vector Databases

  : Pinecone, Weaviate, or similar leading platforms.

• Prompt Engineering

  .

• Security & Ethics

  : AI/LLM solution security, Responsible AI principles.

• Version Control

  : GitHub.

• Databases

  : SQL/NoSQL.

• Good to have

  :
• Reinforcement learning, multi-modal AI systems, complex reasoning frameworks (e.g., ReAct, Chain-of-Thought, planning algorithms).
• Experience in large-scale multi-agent coordination.
• Knowledge of deep learning frameworks such as PyTorch or TensorFlow.
• Experience with Feature Stores.

Skills Matrix

Python (must)

ML Engineering (must)

SQL/NoSQL (must)

LLM Frameworks (LangChain, LangGraph) (must)

MLOps/LLMOps (Docker/K8s, CI/CD, Automated Testing, Monitoring, Observability, Model Registries, Data Versioning)  (must)

Cloud (AWS/Azure/GCP)  (must)

Vector Databases (e.g., Pinecone, Weaviate, or similar leading platforms)  (must)

Prompt Engineering (must)

AI Agent Design (Chains, Tools) (must)

A2A / MCP (highly preferred)

GitHub (must)

Security (AI/LLM, Responsible AI)

Foundation Model APIs

Reinforcement Learning

Multi-modal AI systems

Complex Reasoning Frameworks

PyTorch / TensorFlow

Feature Stores

Instructions for Candidates

Please send your resume to jobs@SoothsayerAnalytics.com

About Us:

Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning solutions—spanning predictive analytics, optimization, NLP, and GenAI—that help leading enterprises forecast, automate, and gain a competitive edge. Join us to tackle high-impact, cross-industry projects where your ideas move rapidly from concept to production, shaping the future of data-driven decision-making.