AI/ML Engineer

VentraGate

2 years

0 Lacs

coimbatore tamil nadu india

Posted:13 hours ago| Platform:

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Skills Required

ai ml design reasoning planning scalability efficiency code model monitoring troubleshooting research pytorch tensorflow engineering tuning aws gcp azure containerization docker data algorithms debugging communication reinforcement learning mlflow api flask fastapi

Work Mode

On-site

Job Type

Full Time

Job Description

Key Responsibilities

Design, develop, and deploy AI/ML models with a strong focus on Generative AI (LLMs, diffusion models, multimodal AI).
Build, fine-tune, and evaluate large language models for domain-specific applications.
Develop agentic AI systems (autonomous/assistive agents) capable of reasoning, planning, and tool use.
Integrate AI/ML models into production systems, APIs, and applications.
Conduct experiments, benchmark models, and optimize for scalability, efficiency, and cost.
Write clean, modular, and well-documented code for ML pipelines and AI services.
Implement best practices for model monitoring, observability, and troubleshooting in production.
Contribute to research exploration, technical discussions, and knowledge sharing within the AI/ML team.

What We’re Looking For

1–2 years of experience
as an AI/ML Engineer.
Strong hands-on expertise with
Python
and popular ML/AI frameworks (
PyTorch, TensorFlow, Hugging Face
).
Solid understanding of
Generative AI concepts
(LLMs, embeddings, prompt engineering, RAG).
Familiarity with
agentic AI frameworks
(LangChain, LlamaIndex, Haystack, AutoGen, etc.).
Experience in building, fine-tuning, or deploying
transformer-based models
.
Knowledge of
vector databases
(Pinecone, Weaviate, FAISS, Milvus).
Understanding of
cloud platforms
(AWS, GCP, Azure) and containerization (
Docker, Kubernetes
).
Strong foundation in
data structures, algorithms, and ML fundamentals
.
Excellent
problem-solving, debugging, and communication skills
.

Nice to Have

Experience with
reinforcement learning, multi-agent systems, or AI reasoning frameworks
.
Knowledge of
MLOps tools
(MLflow, Weights & Biases, Kubeflow).
Exposure to
multimodal AI
(text, image, audio, video).
Familiarity with
API development
(Flask, FastAPI) for serving AI models.
Contributions to
open-source AI projects
or
published research
.

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