AI Engineer (1+ Year Experience)

We are seeking a highly motivated AI Engineer with approximately one year of hands-on experience to join our AI team and help build modern, production-grade AI applications. This role is designed for candidates who have a formal academic background in Artificial Intelligence and have actively worked with contemporary AI systems beyond coursework.

You will work on real-world AI products involving Generative AI, Agentic AI systems, LLM-powered applications, Retrieval-Augmented Generation (RAG), model orchestration, observability, and secure deployment. The role requires strong technical curiosity, fast learning ability, and comfort with the rapidly evolving AI ecosystem.

This is not a research-only or theoretical role. We are looking for an engineer who can design, build, evaluate, and operate AI systems end-to-end.

Design, develop, and deploy modern AI applications using Large Language Models (LLMs) and multimodal models

Build and optimize RAG pipelines using vector databases, embeddings, chunking strategies, and evaluation metrics

Develop Agentic AI systems, including tool-using agents, planners, memory systems, and multi-agent workflows

Integrate AI models into production systems via APIs, backend services, and event-driven architectures

Work with multiple model types (open-source and proprietary): text, vision, audio, multimodal, and embedding models

Implement model observability and monitoring, including logging, tracing, evaluation, hallucination detection, and performance tracking

Apply security, privacy, and compliance best practices for AI systems (data handling, prompt safety, PII protection, access control)

Stay current with fast-moving AI developments, frameworks, and emerging standards (e.g., MCP, agent protocols, tooling ecosystems)

Collaborate with product, engineering, and leadership teams to translate business needs into AI solutions

Contribute to documentation, internal tooling, and AI best practices

Mandatory Qualifications

Bachelor’s degree (or higher) in Artificial Intelligence, Machine Learning, Computer Science (AI specialization), or a closely related field

Around 1 year of hands-on experience building or deploying AI systems (industry, startup, internship, or serious project experience)

Technical Skills

Strong understanding of Generative AI and LLMs (architecture, prompting, limitations, evaluation)

Practical experience with RAG systems, including vector databases and embedding models

Experience building agentic workflows (single or multi-agent systems)

Familiarity with modern AI frameworks and tooling (e.g., LangChain, LlamaIndex, Haystack, custom agent frameworks)

Knowledge of model orchestration, tool calling, and structured outputs

Experience working with multiple model providers and open-source models

Understanding of AI observability, monitoring, and evaluation techniques

Awareness of AI security, privacy, and responsible AI practices

Proficiency in Python and ability to write clean, maintainable code

Familiarity with REST APIs, cloud services, and basic deployment concepts

Soft Skills

Strong problem-solving mindset and ability to learn quickly

Clear communication and documentation skills

Ability to work independently and take ownership of tasks

Genuine interest in building real-world AI products, not just prototypes

Opportunity to work on cutting-edge AI systems used in real production environments

Exposure to end-to-end AI product development, not siloed tasks

Mentorship from senior engineers and architects

Fast-paced learning environment aligned with the latest AI advancements

Flexible work arrangements (remote)

Career growth pathway toward Senior AI Engineer or AI Architect roles

Interested candidates should submit:

An updated resume/CV

A brief cover note explaining:

Your hands-on experience with AI systems

Any real projects, products, or deployments you have worked on

Links to GitHub repositories, portfolios, or demos (strongly preferred)

Shortlisted candidates may be asked to discuss or demonstrate past AI work as part of the evaluation process.