AI Engineer

Role Overview: We're seeking a motivated entry-level AI Engineer to join our AI team. You’ll help build LLM-powered applications and autonomous AI agents that go into the customized software we deliver for clients across financial services, insurance, telecom, retail, and utilities.

The role is hands-on, involving turning business problems into working AI features such as retrieval-augmented applications, document-processing systems, and multi-step agents, and getting them into production alongside our senior engineers. It’s a strong opportunity for a recent graduate to build real AI engineering experience on live client projects.

Key Responsibilities:

LLM Application Development

• Prompt Engineering: Develop and iterate on prompting strategies for reliable, task-specific outputs.
• LLM Integration: Build applications on top of large language models for document processing, customer support automation, and financial analysis.
• Retrieval-Augmented Generation (RAG): Assist in building RAG pipelines which includes chunking, embeddings, vector search, and grounding responses in client data.
• Evaluation & Guardrails: Help test LLM outputs for accuracy and safety, and apply basic guardrails such as PII handling, prompt-injection checks, and output validation.

Agentic AI Development

• Agent Development: Learn to build autonomous and multi-agent systems that plan and execute multi-step tasks.
• Tool Integration: Create and integrate tools and APIs that agents use to interact with external systems.
• Orchestration: Work with agent frameworks and orchestration tooling to compose reliable agent workflows.

Model Development

• Model Development: Assist in training, fine-tuning, and optimizing machine learning models for classification, regression, and NLP tasks.
• Model Evaluation: Support evaluation of model performance and help interpret results.
• Data Preparation: Prepare and curate datasets for training and evaluation, including labeling, cleaning, and formatting.

AI Application Engineering

• Python Development: Build Python applications, microservices, and APIs that serve AI models and agents.
• Deployment Support: Support deployment of AI applications to production, including basic CI/CD and containerized workflows.
• Monitoring: Help set up monitoring and alerting for model performance and application health.

Cross-Functional Responsibilities

• Research & Development: Keep current with AI/ML and agent developments; experiment with new models, frameworks, and techniques.
• Collaboration: Work with senior engineers, product managers, and business stakeholders to identify and shape AI opportunities.
• Documentation: Document prompts, agent designs, model architectures, and runbooks so others can build on your work.

Required Qualifications:

• Education: Diploma or Bachelor’s Degree in Computer Science, Computing, Software Engineering, Data Science, Artificial Intelligence, Mathematics, Statistics, or a related field. Recent graduates are welcomed.
• Academic Foundation: Coursework in machine learning, statistics, data structures, and algorithms.
• Programming Skills: Proficiency in Python and working knowledge of SQL, with academic or personal project experience.
• ML Foundation: Understanding of core machine learning concepts and how models are trained and evaluated.
• LLM Curiosity: Genuine interest in large language models, prompting, and building applications on top of them.
• Learning Mindset: Strong desire to learn AI engineering and agent frameworks, with the adaptability to pick up new tools quickly.
• Problem Solving: Analytical thinking and a systematic approach to AI and data problems.
• Communication: Good written and verbal communication for collaborative work and stakeholder discussions.

Preferred Qualifications

• LLM Experience: Personal projects, coursework, or internship experience building with LLMs (e.g. Anthropic, OpenAI, or open models).
• Agent Development: Familiarity with agent frameworks such as Strands and LangChain, and orchestration with LangGraph.
• RAG & Vector Search: Exposure to embeddings and vector databases such as OpenSearch, pgvector, or FAISS.
• Deep Learning Frameworks: Academic projects using PyTorch or TensorFlow.
• Data Science Libraries: Experience with Pandas and NumPy.
• Cloud Exposure: Basic familiarity with AWS (preferred), GCP, or Azure, and with cloud AI services such as Amazon Bedrock.
• Containers & Version Control: Working knowledge of Docker and Git.
• Databases: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB) databases.