INFOPARK

Immediate Opening for Data Engineer

We are seeking a highly skilled Data & AI Engineer with expertise in Machine Learning, Predictive Analytics, Natural Language Processing (NLP), Computer Vision, Large Language Models (LLMs), and Databricks. The ideal candidate will design, develop, and deploy scalable AI and data solutions that enable advanced analytics, intelligent automation, and AI-driven business outcomes.

Experience 5-15 Years

Key Responsibilities

Design, develop, and deploy end-to-end AI and Machine Learning solutions.

Build predictive analytics models for forecasting, classification, recommendation, and optimization use cases.

Develop and implement NLP solutions, including text analytics, document intelligence, sentiment analysis, and conversational AI.

Build Computer Vision solutions for image classification, object detection, OCR, and video analytics.

Develop and deploy Generative AI and LLM-based applications, including RAG, AI agents, and conversational assistants.

Design and implement scalable data pipelines and AI workflows using Databricks. Develop feature engineering pipelines, model training workflows, and model evaluation frameworks.

Implement MLOps practices for model deployment, monitoring, retraining, and governance.

Work with structured, semi-structured, and unstructured data from multiple sources. Collaborate with business, data engineering, and application teams to translate business

Immediate Opening for Gen AI SSE Position

Gen AI Application Developer – Python – SSE (3-5 Yrs.) Programming: Strong Python programming skills; experience with TypeScript/JavaScript is a plus. GenAI Frameworks & Ecosystem: Hands-on experience with frameworks like LangChain, LangGraph, LlamaIndex, or similar agentic AI libraries. Knowledge of MCP, A2A protocols, output evaluation libraries are good to have. LLMs & Prompt Engineering: Solid understanding of large language models (OpenAI, Anthropic, Cohere, etc.), prompt engineering, tool invocation, and context management. RAG & Vector DBs: Proficiency with different patterns of Retrieval Augmented Generation (RAG), vector databases (Pinecone, Weaviate, etc.), embeddings, and chunking strategies. API Development: Experience designing and building RESTful APIs and integrating AI services. Cloud Platforms: Exposure to atleast 1 cloud providers (AWS, Azure, GCP) Best Practices: Knowledge of software engineering best practices, design patterns, and code optimization. Problem-Solving: Excellent ana

Immediate Opening for Module Lead /Technical Lead

Technical Requirements

Core AI/ML: Expert-level proficiency in Python and leading AI/ML frameworks, including TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.

Strong understanding of supervised, unsupervised, and deep learning techniques, with experience developing, evaluating, and optimizing production-grade machine learning models.

LLMs & Generative AI: Hands-on experience with transformer-based foundation models (GPT, Llama, BERT, Mistral, etc.), prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning (LoRA/QLoRA), embedding models, and agentic AI workflows for domain-specific applications.

Data Engineering: Proficiency in working with structured, semi-structured, and unstructured data (JSON, XML, CSV, Parquet, documents).

Strong knowledge of SQL/NoSQL databases, vector databases, and graph data structures.

Experience with document processing, OCR pipelines, and specialized data extraction is a strong advantage.

Backend & AI Deployment: Experience exposing AI/ML models through



Roles and Responsibility

This role is not just about building AI models—it is about building the intelligence that powers the industrial backbone of the future.

You will solve real-world, high-impact challenges, from ensuring product design and manufacturing are safe and compliant to predicting supply chain disruptions before they impact critical operations or national security capabilities.

Data Exploration & Analysis Regulatory Text Interpretation Rule Formalization Natural Language Querying Retrieval-Augmented Generation (RAG) & Knowledge Engineering Forecasting & Demand Prediction Classification & Risk Scoring Data Engineering for AI AI Model Orchestration Performance Optimization Model Validation & Quality Assurance

Immediate Opening for Data Engineer (Module Lead/Technical Lead)

Technical Requirements

Experience -5 to 10 years

Core AI/ML: Expert-level proficiency in Python and leading AI/ML frameworks, including TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.

Strong understanding of supervised, unsupervised, and deep learning techniques, with experience developing, evaluating, and optimizing production-grade machine learning models.

LLMs & Generative AI: Hands-on experiencea with transformer-based foundation models (GPT, Llama, BERT, Mistral, etc.), prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning (LoRA/QLoRA), embedding models, and agentic AI workflows for domain-specific applications.

Data Engineering: Proficiency in working with structured, semi-structured, and unstructured data (JSON, XML, CSV, Parquet, documents).

Strong knowledge of SQL/NoSQL databases, vector databases, and graph data structures.

Experience with document processing, OCR pipelines, and specialized data extraction is a strong advantage.

Backend & AI Deployment: Experience exposing AI/ML models through

Roles and Responsibility

This role is not just about building AI models—it is about building the intelligence that powers the industrial backbone of the future.

You will solve real-world, high-impact challenges, from ensuring product design and manufacturing are safe and compliant to predicting supply chain disruptions before they impact critical operations or national security capabilities.

Data Exploration & Analysis Regulatory Text Interpretation Rule Formalization Natural Language Querying Retrieval-Augmented Generation (RAG) & Knowledge Engineering Forecasting & Demand Prediction Classification & Risk Scoring Data Engineering for AI AI Model Orchestration Performance Optimization Model Validation & Quality Assurance