Senior AI/ML Engineer

Exprience: 8-12 Years

Role: Senior AI/ML Engineer / AI Solutions Engineer

Location: Flexible/Hybrid

Employment Type: Full-Time

Preferred candidates: IIT, NIT, IIIT, BIT, VIT

Role Overview

We are seeking an experienced AI/ML Engineer with strong fundamentals in Machine Learning, Deep Learning, and Python development. The ideal candidate should have hands-on experience building ML/DL models from scratch, taking solutions from raw data through production deployment, and working on modern Generative AI applications.

This role requires a practitioner who understands the complete AI lifecycle, including data preparation, feature engineering, model training, evaluation, optimization, deployment, and monitoring.

Candidates with only prompt engineering or OpenAI API integration experience without core ML/DL expertise will not be considered.

Key Responsibilities

Machine Learning & Deep Learning

• Design, develop, train, and deploy machine learning and deep learning models from scratch.
• Work with structured, semi-structured, and unstructured datasets at scale.
• Perform feature engineering, feature selection, and data preprocessing.
• Develop and optimize predictive and classification models.
• Conduct hyperparameter tuning and model performance optimization.
• Evaluate models using appropriate metrics and validation strategies.

Generative AI

• Design and implement enterprise-grade Generative AI solutions.
• Build Retrieval Augmented Generation (RAG) pipelines.
• Fine-tune Large Language Models (LLMs) using techniques such as LoRA and QLoRA.
• Develop prompt engineering strategies for business applications.
• Work with embeddings, vector databases, and semantic search architectures.
• Implement evaluation frameworks, hallucination control, and AI guardrails.

Production Deployment

• Deploy and manage ML/AI solutions in production environments.
• Build scalable APIs and inference services using FastAPI or Flask.
• Containerize applications using Docker.
• Work with Kubernetes for orchestration and scaling.
• Implement CI/CD pipelines for AI systems.
• Monitor model performance and operational reliability.

Knowledge Graphs (Good to Have)

• Build and integrate Knowledge Graph solutions.
• Work with Neo4j, RDF, SPARQL, Graph Databases, and GraphRAG architectures.
• Implement entity extraction, entity linking, and relationship extraction workflows.

Mandatory Skills

Programming

• Expert-level Python development
• Strong software engineering practices
• Object-Oriented Programming (OOP)
• API development

Machine Learning

Hands-on experience implementing and deploying:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost
• LightGBM
• Clustering techniques
• Ensemble learning methods

Deep Learning

Experience building and training models using:

• CNNs (Convolutional Neural Networks)
• RNNs
• LSTM Networks
• Transformers
• Autoencoders

AI/ML Frameworks

Must have strong practical experience with:

• PyTorch (Preferred)
• TensorFlow
• Keras
• Scikit-learn
• NumPy
• Pandas

Model Development Lifecycle

Strong experience in:

• Data Collection
• Feature Engineering
• Model Training
• Hyperparameter Optimization
• Model Evaluation
• Model Explainability
• Production Deployment

Generative AI Requirements

Candidates should have hands-on experience with:

• RAG (Retrieval Augmented Generation)
• LLM Fine-Tuning
• Hugging Face Ecosystem
• Embedding Models
• Vector Databases

Preferred vector databases:

• Pinecone
• Weaviate
• Chroma
• Milvus
• FAISS

Experience with:

• Prompt Engineering
• Context Management
• LLM Evaluation Frameworks
• AI Safety & Guardrails

Cloud & MLOps

Experience with one or more cloud platforms:

• AWS
• Azure
• GCP

MLOps experience in:

• Docker
• Kubernetes
• CI/CD
• Model Monitoring
• Experiment Tracking
• ML Pipelines

Good to Have

• Knowledge Graphs
• Neo4j
• RDF
• SPARQL
• GraphRAG
• Entity Linking
• Relationship Extraction
• LangChain
• LlamaIndex
• MLOps Platforms
• Azure ML
• SageMaker

Nice to Have

• Agentic AI
• Multi-Agent Systems
• Model Context Protocol (MCP)
• Reinforcement Learning
• Explainable AI (XAI)
• LLM-as-a-Judge Frameworks
• AI Governance & Responsible AI