GenAI professional -Agentic AI

We are Hiring GenAI professional with proven production-grade project deployment experience with strong expertise in Agentic AI.

About the Role

We are looking for a

GenAI professional

with strong experience in

NLP, Computer Vision, and LLM-based agentic systems . In this role, you will design, build, fine-tune, and deploy

production-grade LLM agents

and

multi-modal AI applications

that solve real-world business challenges. You will play a key role in shaping

agent design, orchestration, and observability , ensuring enterprise-grade scalability, robustness, and performance.

Key Responsibilities

Model & Agent Design

Conceptualize, design, and implement

LLM-powered agents

and

NLP solutions

tailored to business needs.

Build

multi-agent and multi-modal AI applications / frameworks , ensuring interactivity, latency optimization, failover, and usability.

Apply advanced design principles for

structured outputs, tool usage, speculative decoding, AST-Code RAG, streaming, and async / sync processing .

Hands-on Coding & Development

Write, test, and maintain clean, scalable, and efficient

Python

code for LLMs and AI agents.

Implement

fine-tuning, embeddings, and prompt engineering

with a focus on cost, latency, and accuracy.

Integrate models with

vector databases

(Milvus, Qdrant, ChromaDB, CosmosDB, MongoDB).

Performance & Monitoring

Monitor and optimize

LLM agents

for latency, scalability, robustness, and explainability.

Implement

observability and guardrails strategies

for enterprise-safe AI deployments.

Handle

model drift, token consumption optimization, and error recovery mechanisms .

Research & Innovation

Read, interpret, and implement

AI / Agent research papers

into practical production-ready solutions.

Stay ahead of academic and industry trends in

Agentic AI, multimodal AI, orchestration frameworks, and evaluation methodologies .

Experiment with

new AI orchestration tools, evaluation frameworks, and observability platforms

(Arize or similar).

Debugging & Issue Resolution

Diagnose and resolve

model inaccuracies, system integration issues, and performance bottlenecks .

Apply advanced debugging techniques to troubleshoot deployment errors, data inconsistencies, and unexpected agent behaviors.

Continuous Learning & Adaptability

Quickly unlearn outdated practices and adapt to

emerging GenAI and Agentic AI technologies .

Contribute to a culture of innovation by

experimenting, prototyping, and scaling

cutting-edge AI solutions.

Required Skills & Experience

5–9 years total experience , with

4+ years in NLP, CV, and LLMs .

Strong expertise in

GenAI, LLMs, RAG pipelines, embeddings, and vector databases .

Proficiency in

Python

with strong debugging and system design skills.

Hands-on experience with

Agentic AI frameworks

(LangChain Agents, AutoGen, CrewAI, Temporal, DSPy).

Proven record of

production-grade AI deployments

(not just POCs).

Cloud experience :

Azure (preferred), AWS, or GCP .

Knowledge of

AI orchestration, evaluation, guardrails, and observability tools

(Arize, Weights & Biases, etc.).