AI Engineer

Tech ECS Limited; Kolkata Metropolitan Area; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Tech ECS Limited seeks an innovative AI Engineer to design, develop, and deploy AI/ML and Generative AI solutions with full-stack capabilities. You will build scalable web apps, integrate ML models, and deploy on cloud platforms, collaborating with stakeholders to solve business challenges.

The role requires hands-on experience with Python, ML frameworks, React.js, Node.js, and cloud services, plus strong collaboration across product and engineering teams.

Qualifications

• Bachelor's or Master's in CS/AI/Data Science or related field.
• 1+ years of AI/ML development experience.
• Strong Python and ML frameworks.
• Experience with Generative AI, LLMs, and RAG.
• Experience with React.js, Node.js, and cloud platforms.

Responsibilities

• Design, develop, and implement AI/ML models and intelligent applications.
• Build and optimize ML pipelines for training, testing, deployment, and monitoring.
• Develop and integrate Generative AI, LLMs, AI copilots, and intelligent assistants.
• Fine-tune, evaluate, and monitor AI models for accuracy, reliability, and cost efficiency.
• Implement RAG, prompt engineering, embeddings, semantic search, and vector databases.
• Develop AI solutions with enterprise apps via REST APIs and third-party services.
• Design and develop responsive front-end apps using React.js, JavaScript, and TypeScript.
• Develop scalable backend services and APIs using Node.js, Express.js, and NestJS.
• Design and integrate databases including MongoDB, SQL, PostgreSQL, and NoSQL.
• Build end-to-end AI-powered apps by integrating models, APIs, frontend, backend, and databases.
• Deploy AI and full-stack apps on cloud platforms (Azure/AWS/GCP).
• Work with cloud AI services, compute, storage, databases, networking, and security.

Skills

AI/ML development

Python

JavaScript/TypeScript

React.js

Node.js

REST APIs

Cloud platforms

MLOps

Docker & Kubernetes

Vector databases

Education

Bachelor's or Master's in CS/AI/Data Science/Engineering

Tools

TensorFlow

PyTorch

Scikit-learn

MongoDB

PostgreSQL

Job description

We are seeking an innovative and results-driven AI Engineer to design, develop, and deploy artificial intelligence, machine learning, Generative AI, and full-stack solutions that solve real business challenges. The ideal candidate will have experience building AI-powered applications, training and integrating machine learning models, developing LLM and Generative AI solutions, building scalable web applications, and deploying enterprise solutions on cloud platforms.

Key Responsibilities

• Design, develop, and implement AI/ML models and intelligent applications.
• Build and optimize machine learning pipelines for training, testing, deployment, and monitoring.
• Develop and integrate Generative AI, Large Language Models (LLMs), AI-powered copilots, and intelligent assistants.
• Fine-tune, evaluate, and monitor AI models to ensure accuracy, reliability, performance, and cost efficiency.
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, semantic search, and vector database solutions.
• Develop and integrate AI solutions with enterprise applications through REST APIs and third-party services.
• Design and develop responsive front-end applications using React.js, JavaScript, and TypeScript.
• Develop scalable backend services and APIs using Node.js, Express.js, and NestJS.
• Design and integrate databases including MongoDB, SQL, PostgreSQL, and other NoSQL databases.
• Build end-to-end AI-powered applications by integrating AI models, APIs, frontend applications, backend services, and databases.
• Deploy AI and full-stack applications using cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
• Work with cloud-based AI services, compute, storage, databases, networking, and security services.
• Implement containerized application deployments using Docker and Kubernetes.
• Build and maintain CI/CD pipelines for AI and application deployment.
• Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management.
• Ensure AI solutions comply with security, privacy, governance, and responsible AI standards.
• Monitor production systems, troubleshoot issues, and continuously improve application and model performance.
• Collaborate with business stakeholders, product managers, UI/UX designers, and engineering teams to identify AI use cases and deliver business solutions.
• Prepare technical designs, prototypes, proof-of-concepts, and architecture for AI-powered applications.
• Stay current with advancements in AI, machine learning, Generative AI, cloud technologies, and modern software engineering practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 1+ years of experience in AI/ML development, software engineering, Generative AI, or a related field.
• Strong programming skills in Python and experience with AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong knowledge of Generative AI technologies, LLMs, prompt engineering, RAG architectures, embeddings, and vector search.
• Good programming experience in JavaScript/TypeScript.
• Hands-on experience with React.js for developing modern web applications.
• Hands-on experience with Node.js and backend frameworks such as Express.js or NestJS.
• Experience working with APIs, databases, and cloud platforms.
• Experience with MongoDB and/or SQL-based databases.
• Understanding of MLOps practices, model deployment, monitoring, and CI/CD.
• Understanding of Docker and containerized application deployment.
• Strong analytical, problem-solving, debugging, and communication skills.
• Ability to work collaboratively with product, business, engineering, and DevOps teams.

Preferred Qualifications

• Experience with Azure AI Services, Azure OpenAI Service, Microsoft Copilot Studio, or Microsoft Fabric.
• Hands-on expertise with vector databases such as Pinecone, Weaviate, Azure AI Search, or similar technologies.
• Experience with LangChain, AI Agents, and Agentic AI architectures.
• Experience building enterprise AI copilots, intelligent assistants, recommendation systems, or automation solutions.
• Strong experience with React.js, Node.js, NestJS, Express.js, and MongoDB.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
• Experience implementing enterprise AI governance and responsible AI practices.
• Experience with CI/CD pipelines and cloud-native application development.
• AI/ML or cloud certifications are a plus.

Technical Skills

Programming: Python, SQL, JavaScript, TypeScript

Frontend: React.js, HTML, CSS, TypeScript

Backend: Node.js, Express.js, NestJS

AI/ML Frameworks: PyTorch, TensorFlow, Scikit-learn

Generative AI: OpenAI, Azure OpenAI, LLMs, Prompt Engineering, RAG, Embeddings

Cloud Platforms: Microsoft Azure, AWS, Google Cloud

Databases: SQL, PostgreSQL, MongoDB, NoSQL, Vector Databases

Vector Databases: Pinecone, Weaviate, Azure AI Search

DevOps/MLOps: Git, Docker, Kubernetes, CI/CD Pipelines, Model Deployment, Model Monitoring

APIs & Integration: REST APIs, Third-Party APIs, AI APIs, Microservices

Security: OAuth, JWT, RBAC, API Security, Responsible AI

Success Measures

• Delivery of scalable and production-ready AI and full-stack solutions that drive measurable business value.
• Improved process efficiency through AI automation and intelligent applications.
• Successful implementation of Generative AI, LLM, RAG, and AI-powered solutions.
• High model accuracy, application reliability, performance, and user satisfaction.
• Successful integration of AI capabilities with enterprise applications and existing systems.
• Efficient deployment and operation of AI applications across cloud environments.
• Secure, maintainable, and scalable application architecture.
• Effective implementation of security, governance, privacy, and responsible AI standards.
• Continuous improvement of model and application performance based on production feedback and business requirements.

Reporting to: Chief Technology Officer