AI/ML Engineer – Agentic Systems ( 3 to 8 yrs )

AI/ML Engineer – Agentic Systems Experience:

3–8 Years Location:

Remote Mode of Engagement:

Full-time No. of Positions:

4 Educational Qualifications:

B.E./B.Tech/M.E./M.Tech in Computer Science, AI/ML, Data Science, or related field Industry:

IT – AI/ML Services Notice Period:

Immediate

What We Are Looking For 3–8 years of hands-on experience in AI/ML with strong practical exposure to

Transformer-based models . Proven experience in

fine-tuning, optimizing, and deploying LLMs

(BERT, T5, GPT-style, LLaMA, Mistral, etc.). Strong

Python

skills for model training, inference pipelines, APIs, and system integration. Real-world experience working with

agentic / multi-agent AI systems

(planner–executor, supervisor–worker patterns, tool-using agents). Ability to independently own

model development → experimentation → production deployment . Experience handling

latency, cost, scalability, and monitoring

in production AI systems.

Responsibilities Design, fine-tune, and deploy

Transformer-based models

for NLP, reasoning, information extraction, and generation tasks. Build and manage

multi-agent AI systems

for task decomposition, orchestration, and decision-making. Implement

efficient inference pipelines

with batching, caching, quantization, and latency optimization. Develop production-grade

REST APIs

using FastAPI/Flask and containerize services using Docker. Collaborate with internal teams and clients to convert business requirements into scalable AI solutions. Monitor model performance, accuracy, drift, and cost; continuously improve system reliability. Stay up to date with advancements in

transformer architectures, LLMs, and agent-based AI systems .

Qualifications Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related discipline. Minimum

3 years of hands-on experience

with Transformer architectures. Strong working experience with

Hugging Face Transformers

and

PyTorch

(preferred) or TensorFlow/JAX. Solid understanding of

attention mechanisms, encoder–decoder models, embeddings, and fine-tuning strategies . Familiarity with

multi-agent frameworks

(LangChain, LangGraph, AutoGen, CrewAI, or similar). Experience with

REST APIs, Docker, Git , and cloud deployment on

AWS or GCP . Strong communication skills with the ability to explain complex AI concepts to non-technical stakeholders.