The Role
Designs, builds, evaluates, and deploys machine learning, deep learning, generative AI, and agentic applications. Develops RAG pipelines, fine-tunes models, creates evaluation frameworks and guardrails, automates data and MLOps pipelines, and serves models through scalable APIs. The role also involves optimizing accuracy, latency, and cost while applying current AI/ML advances to practical use cases.
Summary Generated by Built In
The AI Engineer designs, builds, and operationalises machine learning and generative AI systems, taking them from
prototype to production. The role covers model development, LLM and agentic application engineering, and the data
and deployment pipelines that keep these systems accurate, scalable, and reliable.
• Build, evaluate, and deploy machine learning and deep learning models for production.
Requirements
Benefits
Key Responsibilities:
• Build, evaluate, and deploy machine learning and deep learning models for production.
• Develop LLM-based and agentic applications, including RAG pipelines and tool use.
• Fine-tune and optimise models for accuracy, latency, and cost using PEFT and quantisation.
• Design evaluation frameworks, benchmarks, and guardrails to safeguard model quality.
• Build and automate data pipelines for training, inference, and model monitoring.
• Package and serve models as scalable APIs with containerisation, CI/CD, and MLOps.
• Stay updated with advancements in AI/ML and apply them to practical use cases.
Requirements
Educational Qualifications:
• B.E./B.Tech, M.Tech, B.Sc., M.Sc., MCA, or an equivalent degree in Computer Science, Information Technology,
Engineering, Mathematics, Statistics, or a related quantitative
field.
• Relevant certifications in cloud, AI/ML, or software
engineering are a plus.
Skillset:
• Strong programming skills in Python.
• Experience with ML/DL frameworks (e.g., scikit-learn, PyTorch,
or TensorFlow).
• Hands-on experience with LLM APIs, prompt engineering, and
agent frameworks (e.g., LangChain, LangGraph, MCP).
• Working knowledge of RAG, embeddings, and vector
databases (e.g., pgvector, FAISS, or Pinecone).
• Experience serving models as APIs (FastAPI, Docker) with
MLOps practices (MLflow, model versioning, monitoring).
• Proficiency with SQL and data pipeline tooling for large
datasets.
• Experience with at least one cloud platform (AWS, Azure, or
GCP).
Soft Skills:
• Adaptability in a fast-evolving technology landscape.
• Strong problem-solving ability with a structured, hands-on
approach.
• Critical thinking to evaluate trade-offs and validate AI
generated outputs.
Benefits
- Opportunity to work with a dynamic and fast-paced engineering IT organization.
- Be part of a company that is passionate about transforming product development with technology.
Skills Required
- B.E., B.Tech, M.Tech, B.Sc., M.Sc., MCA, or equivalent degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, or a related quantitative field.
- Strong programming skills in Python.
- Experience with machine learning and deep learning frameworks such as scikit-learn, PyTorch, or TensorFlow.
- Hands-on experience with LLM APIs, prompt engineering, and agent frameworks such as LangChain, LangGraph, or MCP.
- Working knowledge of RAG, embeddings, and vector databases such as pgvector, FAISS, or Pinecone.
- Experience serving models as APIs using FastAPI and Docker, with MLOps practices including MLflow, model versioning, and monitoring.
- Proficiency with SQL and data pipeline tooling for large datasets.
- Experience with at least one cloud platform: AWS, Azure, or GCP.
- Relevant certifications in cloud, AI/ML, or software engineering.
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The Company
What We Do
CCTech, the Centre for Computational Technologies, is a digital transformation company developing CAD, CFD, artificial intelligence, machine learning, 3D web, augmented reality, digital twin, and enterprise applications. Its product division includes simulationHub, a cloud-based CFD platform, while its consulting division helps engineering organizations with computational engineering, CAD/CFD software development, digital transformation, and related technology services.
