Role Summary:

As an AI/ML Engineer, you will design, build, and deploy machine learning and generative AI solutions that power intelligent applications across enterprise workflows. You will work on building production-grade ML pipelines, integrating large language models (LLMs), and developing scalable AI services that extract insights from structured and unstructured data.

You will collaborate closely with data scientists, product managers, and platform engineers to translate business requirements into robust AI solutions. The role requires strong software engineering fundamentals, practical experience with machine learning systems, and familiarity with modern LLM-based architectures such as Retrieval-Augmented Generation (RAG).

Essential Functions:

1. AI/ML Solution Development

• Design, build, and deploy machine learning models for prediction, classification, clustering, and NLP use cases.

• Implement scalable AI solutions that process structured and unstructured datasets including documents, logs, and transactional data.

• Translate business problems into ML pipelines, including data preparation, model training, evaluation, and deployment.

2. LLM & Generative AI Integration

• Build applications leveraging Large Language Models (LLMs) for tasks such as summarization, document analysis, conversational interfaces, and knowledge retrieval.

• Implement RAG pipelines including document chunking, embedding generation, vector search, and response synthesis.

• Optimize prompts, templates, and evaluation pipelines to improve model reliability, latency, and cost efficiency.

3. Data Engineering & Feature Pipelines

• Build data pipelines for ingestion, preprocessing, feature engineering, and model training.

• Work with structured databases, APIs, and document repositories to extract and prepare datasets for ML workloads.

• Implement scalable data workflows using modern processing frameworks.

4. Model Deployment & MLOps• Deploy machine learning models as APIs or microservices in cloud or containerized environments.

• Implement CI/CD pipelines for model versioning, automated testing, and deployment.

• Monitor model performance, detect drift, and improve reliability through continuous evaluation and retraining.

5. AI Platform Integration

• Integrate AI capabilities into production systems via REST APIs, event pipelines, or workflow orchestration tools.

• Work with vector databases, search systems, and data warehouses to enable AI-driven analytics.

• Optimize inference performance, caching strategies, and resource utilization.

6. Collaboration & Documentation

• Collaborate with product managers, engineers, and data teams to deliver AI-powered features.

• Document model architectures, pipelines, APIs, and engineering decisions.

• Participate in design discussions, sprint planning, and technical reviews.

7. Continuous Improvement & Innovation

• Evaluate emerging AI/ML techniques, frameworks, and tooling to improve solution quality.

• Contribute to internal best practices for ML development, experimentation, and deployment.

• Participate in proof-of-concept development for new AI capabilities.

Essential Qualifications:

B.E./B.Tech. in Computer Science, Artificial Intelligence, Data Science, or related field. Master’s degree in AI/ML/Data Science is a plus.

Experience (3–5 years total):

➢ 3–5 years of experience building machine learning models and deploying them in production environments.

➢ Hands-on experience with Python and ML libraries such as scikit-learn, PyTorch, TensorFlow, or XGBoost.

➢ Experience working with LLM-based applications or NLP systems.

➢ Familiarity with vector databases and retrieval systems used in modern AI applications.

➢ Experience developing REST APIs or microservices for ML models.

➢ Exposure to cloud environments and containerization technologies.

Skills, Knowledge, Abilities & Key Traits:

➢ Programming & Frameworks

o Python (FastAPI, Flask)

o SQL

o Pandas, NumPy, Scikit-learn➢ Machine Learning & AI

o NLP, text classification, information extraction

o LLM application development and prompt engineering

o Retrieval-Augmented Generation (RAG)

➢ Data & Retrieval Systems

o Vector databases (Pinecone, Milvus, Weaviate, pgvector)

o SQL / NoSQL databases

o Elasticsearch or similar search technologies

➢ MLOps & DevOps

o Docker, Kubernetes (basic familiarity)

o CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps)

o Model experiment tracking tools (MLflow, Weights & Biases)

➢ Cloud Platforms

o Experience with at least one major cloud provider (AWS, Azure, or GCP)

➢ Core Competencies:

o Strong problem-solving and analytical thinking

o Ability to work with large datasets and build scalable pipelines

o Strong collaboration and communication skills

o Curiosity about emerging AI technologies and experimentation

o Ability to deliver reliable AI solutions in production environments

➢ Key Traits:

o Ownership mindset and accountability for delivered systems

o Strong engineering discipline and attention to code quality

o Adaptability in fast-evolving AI/ML ecosystems

o Passion for building practical AI solutions that deliver business value