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 team-oriented 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 with a US-based team across time zones

How to Apply

Interested in building intelligent, production-ready AI solutions for a US-based client while working remotely from India?

? Apply directly through LinkedIn or send your updated resume to:

? [email protected]

📌 Senior Full-Stack AI/ML Engineer (India)
🏢 GRhombus Technologies
📍 India