AI/ML Engineer – LLMs, RAG, Reinforcement Learning
Experience: Not required
Employment Type: Full-time
Studies : Mtech or PhD
About the Role
We are building enterprise-grade AI/ML solutions including SLMs, LLMs, RAG-based
knowledge systems, reinforcement learning, and agentic AI,
As a Mid-level AI/ML Engineer, you will design, train, and deploy machine learning models,
collaborate with our product and engineering teams, and ensure scalable integration of AI
models into real-world applications.
This role is ideal for someone with a strong hands-on background in NLP, deep learning,
and reinforcement learning, who is eager to grow by working on cutting-edge AI projects
at scale.
Key Responsibilities
• Design, train, and fine-tune ML/DL models (with focus on transformers, SLMs,
LLMs, and recommender systems).
• Implement RAG pipelines using vector databases (Pinecone, Weaviate, FAISS) and
frameworks like LangChain or LlamaIndex.
• Contribute to LLM fine-tuning using LoRA, QLoRA, and PEFT techniques.
• Work on reinforcement learning (RL/RLHF) for optimizing LLM responses.
• Build data preprocessing pipelines for structured and unstructured datasets.
• Collaborate with backend engineers to expose models as APIs using FastAPI/Flask.
• Ensure scalable deployment using Docker, Kubernetes, AWS/GCP/Azure ML
services.
• Monitor and optimize model performance (latency, accuracy, hallucination rates).
• Use MLflow / Weights & Biases for experiment tracking and versioning.
• Stay updated with the latest research papers and open-source tools in AI/ML.
• Contribute to code reviews, technical documentation, and best practices.
Required Skills & Qualifications
• Strong in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow).
• Solid understanding of NLP and LLM architectures (Transformers, BERT, GPT,
LLaMA, Mistral).
• Practical experience with vector databases (Pinecone, or FAISS or PgVector).
• Basic Knowledge with MLOps tools – Docker, Kubernetes, MLflow, CI/CD.
• Basic Knowledge of cloud platforms (AWS Sagemaker, GCP Vertex AI, or Azure
ML).
• Good grasp of linear algebra, probability, statistics, optimization.
• Strong debugging, problem-solving, and analytical skills.
• Familiarity with Agile methodologies (Scrum, Jira, Git).
Nice-to-Have Skills
• Experience with RLHF pipelines.
• Open-source contributions in AI/ML.
Soft Skills
• Strong communication – able to explain AI concepts to technical & non-technical
stakeholders.
• Collaborative – works well with product, design, and engineering teams.
• Growth mindset – eager to learn new AI techniques and experiment.
• Accountability – able to deliver end-to-end model pipelines with minimal
supervision.
• Can works in a team.
What We Offer
• Work on cutting-edge AI projects with real-world enterprise impact.
• Exposure to LLMs, reinforcement learning, and agentic AI.
• Collaborative Startup & Service culture with room for fast growth.
• Competitive compensation + performance-based incentives.
