Role : Machine Learning Engineer

Job Description :

We are looking for a skilled Machine Learning Engineer to design, develop, optimize, and deploy machine learning, Natural Language Processing (NLP), and Generative AI solutions in production environments. The role involves building scalable Retrieval-Augmented Generation (RAG) pipelines, developing enterprise-ready Large Language Model (LLM) applications, and transforming unstructured data into reliable, high-performance AI systems.

The ideal candidate should have strong Python and machine learning expertise, hands-on experience with deep learning frameworks and LLM orchestration tools, and the ability to build end-to-end AI solutions that perform reliably at scale.

Key Responsibilities :

- Design, develop, train, and deploy machine learning models for NLP, Generative AI, and enterprise applications.

- Build and optimize RAG pipelines, including document ingestion, preprocessing, chunking, embedding generation, retrieval, and response generation.

- Develop LLM-powered applications using frameworks such as LangChain and LlamaIndex, integrating external APIs and enterprise data sources.

- Implement vector search and semantic retrieval solutions using vector databases such as Pinecone and Weaviate.

- Develop efficient data processing pipelines to handle large volumes of structured and unstructured data.

- Optimize model performance, inference latency, embedding quality, and retrieval accuracy for production workloads.

- Build and deploy scalable APIs to integrate machine learning models and LLM applications with enterprise systems.

- Containerize applications using Docker and support deployment and orchestration using Kubernetes.

- Leverage cloud infrastructure such as AWS and GCP to deploy, scale, and maintain machine learning solutions.

- Implement monitoring, logging, testing, and performance evaluation to ensure the reliability of production AI systems.

- Apply MLOps practices to streamline model deployment, versioning, continuous integration, and ongoing performance monitoring.

- Collaborate with data scientists, software engineers, and cross-functional teams to translate business requirements into production-ready AI solutions.

- Maintain clean, reusable, well-documented code and follow best practices for software engineering, security, and scalability.

Required Skills and Qualifications :

- Strong programming experience in Python and familiarity with libraries such as scikit-learn.

- Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.

- Practical knowledge of NLP, Generative AI, Large Language Models (LLMs), and transformer-based architectures.

- Experience building RAG pipelines and integrating LLMs into real-world applications.

- Familiarity with LangChain, LlamaIndex, or similar LLM application frameworks.

- Experience working with OpenAI APIs and vector databases such as Pinecone or Weaviate.

- Strong understanding of embeddings, semantic search, document chunking, retrieval strategies, and vector similarity.

- Experience developing APIs and deploying machine learning models into production environments.

- Knowledge of Docker, Kubernetes, and containerized application deployment.

- Familiarity with cloud platforms such as AWS or GCP.

- Understanding of model evaluation, monitoring, performance optimization, and production debugging.

- Strong problem-solving, analytical, and communication skills.

Preferred Qualifications :

- Experience developing high-throughput, low-latency AI applications.

- Knowledge of MLOps workflows and automated deployment pipelines.

- Experience optimizing RAG systems for large-scale, noisy, or unstructured datasets.

- Familiarity with production monitoring, model versioning, and continuous evaluation of LLM applications.

- Ability to take ownership of end-to-end AI solutions, from data ingestion and experimentation to deployment and ongoing optimization.

Work Arrangement : Remote - Hybrid

Joining : Immediate