We are looking for an AI Engineer to design, build, and deploy intelligent applications powered by machine learning and generative AI. You will work closely with product managers, software engineers, and data scientists to take AI solutions from concept to production.

The ideal candidate has strong programming skills, practical experience with machine learning, and hands-on knowledge of modern LLM and generative AI technologies.

Key Responsibilities

Design, develop, and deploy machine learning and AI-powered applications.
Build and integrate LLM-based applications using models such as GPT, Claude, or open-source alternatives.
Develop RAG (Retrieval-Augmented Generation) pipelines, including document processing, chunking, embeddings, retrieval, and reranking.
Implement prompt engineering and evaluation strategies to improve AI application quality.
Work with vector databases and search technologies for semantic and hybrid search.
Develop APIs and backend services to integrate AI capabilities into production applications.
Monitor AI systems for accuracy, latency, reliability, and cost.
Collaborate with cross-functional teams to translate business requirements into scalable AI solutions.
Experiment with emerging AI/ML techniques and evaluate their applicability to business problems.
Write clean, maintainable, and well-tested production code.
Contribute to technical documentation, architecture decisions, and engineering best practices.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
2–5 years of experience in software engineering, machine learning, or AI engineering.
Strong proficiency in Python.
Good understanding of machine learning concepts and algorithms.
Hands-on experience with frameworks such as PyTorch, TensorFlow, or scikit-learn.
Experience working with REST APIs and backend technologies.
Practical understanding of LLMs, embeddings, vector databases, and RAG architectures.
Familiarity with Git and software development best practices.
Experience deploying applications using cloud platforms such as AWS, Azure, or GCP.

Preferred Qualifications

Experience with LLM orchestration frameworks such as LangChain or LlamaIndex.
Experience with vector databases such as Pinecone, Weaviate, Milvus, or FAISS.
Knowledge of model fine-tuning, LoRA, or parameter-efficient fine-tuning techniques.
Experience with AI evaluation and observability.
Familiarity with Docker and Kubernetes.
Experience building production-grade AI agents or agentic workflows.
Knowledge of MLOps and CI/CD pipelines.
Strong understanding of SQL and data processing pipelines.

Technical Skills

Programming: Python, SQL

Machine Learning: PyTorch, TensorFlow, scikit-learn

Generative AI: LLMs, Prompt Engineering, RAG, Embeddings, Fine-tuning, AI Agents

Databases: PostgreSQL, MongoDB, Vector Databases

Cloud: AWS / Azure / GCP

DevOps: Docker, Kubernetes, CI/CD, Git

Frameworks: FastAPI, LangChain, LlamaIndex

What We Offer

Opportunity to work on real-world AI and generative AI applications.
Exposure to the latest developments in LLMs and AI engineering.
Collaborative and learning-focused engineering environment.
Competitive compensation and benefits.
Opportunities for professional growth and technical leadership.

Equal Opportunity

We are committed to building an inclusive workplace and provide equal employment opportunities to all qualified candidates regardless of background.