Senior Full-Stack AI/ML Engineer

We’re Hiring: Senior Full-Stack AI/ML Engineer

📍 Location: Remote – India

💼 Employment Type: Full-Time

🧠 Experience: 4+ Years in AI/ML

🏢 Hiring Company: GRhombus

🌎 Client: US-Based

🕒 Working Hours: Flexible, with approximately 3–4 hours of overlap with US business hours

About the Opportunity

GRhombus is hiring a Senior Full-Stack AI/ML Engineer for one of our US-based clients.

We are looking for an experienced engineer with strong hands-on expertise in Agentic AI, production-grade chatbots, Retrieval-Augmented Generation (RAG), LLMOps, and traditional Machine Learning-based recommendation systems .

The ideal candidate will have experience building and deploying real-world AI applications, developing stateful multi-agent workflows, and implementing predictive recommendation engines using traditional ML techniques.

If you’re passionate about building scalable, intelligent, production-ready AI solutions, we’d love to hear from you!

Key Responsibilities

• Design, develop, deploy, and maintain production-grade AI chatbots and conversational AI applications.
• Build stateful, multi-agent workflows using LangGraph, LangChain, Google ADK, LlamaIndex, or similar frameworks.
• Develop and optimize RAG pipelines for accurate, context-aware responses.
• Implement agent memory management, conversation state handling, and context retention.
• Establish LLMOps practices, including tracing, observability, structured logging, and evaluation.
• Train, evaluate, deploy, and maintain traditional Machine Learning models.
• Develop recommendation engines using collaborative filtering, content-based filtering, or hybrid approaches.
• Build scalable backend services and APIs using Python and FastAPI.
• Work with PostgreSQL, Redis, and SQL for data management, retrieval, caching, and performance optimization.
• Monitor production systems and continuously improve chatbot reliability, model performance, and recommendation quality.

Required Skills & Experience

1. Agentic AI & Production Chatbots

• Proven hands-on experience building and deploying production-grade chatbots or Agentic AI applications.
• Experience developing stateful, multi-agent workflows.
• Practical knowledge of LangGraph, LangChain, Google ADK, LlamaIndex, or similar frameworks.
• Strong understanding of RAG pipelines and agent memory management.

2. LLMOps, Observability & Evaluation

• Experience with LLM tracing, monitoring, structured logging, and evaluation frameworks.
• Ability to evaluate AI responses for accuracy, relevance, reliability, and quality.
• Experience debugging and monitoring AI applications in production environments.

3. Traditional Machine Learning & Recommendation Systems

• Hands-on experience training, evaluating, deploying, and maintaining traditional ML models.
• Experience building recommendation engines using collaborative filtering, content-based filtering, or hybrid techniques.
• Understanding of feature engineering, model evaluation metrics, recommendation quality, and performance optimization.

4. Backend Development & Databases

• Strong Python programming skills.
• Experience developing backend APIs using FastAPI.
• Proficiency in SQL and practical experience with PostgreSQL.
• Experience using Redis for caching, session management, or related applications.

Good-to-Have Skills

• Exposure to Snowflake or Databricks AI agents.
• Experience with cloud deployment, CI/CD, containerization, and scalable AI architectures.
• Familiarity with AI/ML model lifecycle management and production monitoring.

Important Eligibility Criteria

Candidates must have hands-on experience in both of the following areas:

Developing and deploying production-grade AI chatbots or Agentic AI applications.

Building and deploying traditional ML-based recommendation systems.

Please note: Experience limited to prompt engineering, basic LLM API integrations, or Generative AI without practical recommendation-system development will not meet the core requirements.

Candidates should be able to clearly explain their project architecture, individual technical contributions, deployment experience, and measurable real-world outcomes.

Work Arrangement

• Work Location: Remote – India
• Client: US-based
• Employment: Full-Time
• Working Hours: Flexible, with approximately 3–4 hours of overlap with US business hours
• Collaboration: Comfortable working…