Senior AI/ML Engineer

NexionPro Services; Bengaluru; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

NexionPro Services in Bengaluru is seeking a Senior AI/ML Engineer to drive end-to-end AI initiatives, including data prep, model training, deployment, and monitoring. The role emphasizes modern Generative AI applications and scalable ML systems.

The candidate will work on RAG pipelines, LLM fine-tuning, and prompt strategies, with emphasis on production readiness, embeddings, and guardrails. Hybrid work flexibility offered.

Qualifications

• Strong fundamentals in Machine Learning, Deep Learning, and Python development.
• Hands-on experience building ML/DL models from scratch and deploying to production.
• Experience with Generative AI, RAG pipelines, and LLM fine-tuning.
• Knowledge of embeddings, vector databases, and semantic search architectures.
• Proficient in model evaluation, hyperparameter tuning, and monitoring.

Responsibilities

• Design, develop, train, and deploy ML/DL models from scratch.
• Work with structured, semi-structured, and unstructured data at scale.
• Perform feature engineering and data preprocessing.
• Develop and optimize predictive and classification models.
• Deploy AI solutions in production and monitor performance.

Skills

Strong software engineering practices

OOP

PyTorch

TensorFlow

Scikit-learn

NumPy

RNNs/LSTMs/Transformers

Production deployment

Tools

PyTorch

TensorFlow

Scikit-learn

NumPy

FastAPI/Flask

Docker

Kubernetes

Job description

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

• 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

• Strong software engineering practices
• Object-Oriented Programming (OOP)

Hands-on experience implementing and deploying:

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

Experience building and training models using:

• RNNs
• LSTM Networks
• Transformers

AI/ML Frameworks

Must have strong practical experience with:

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

Model Development Lifecycle

Strong experience in:

• Data Collection
• Hyperparameter Optimization
• Production Deployment

Generative AI Requirements

Candidates should have hands-on experience with:

• RAG (Retrieval Augmented Generation)
• LLM Fine-Tuning
• Vector Databases

Preferred vector databases:

• Pinecone
• Weaviate
• Chroma
• Milvus
• FAISS

Experience with:

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

Experience with one or more cloud platforms: