Job Description
CES has 26+ years of experience in delivering Software Product Development, Quality Engineering, and Digital Transformation Consulting Services to Global SMEs & Large Enterprises. CES has been delivering services to some of the leading Fortune 500 Companies including Automotive, AgTech, Bio Science, EdTech, FinTech, Manufacturing, Online Retailers, and Investment Banks. These are long-term relationships of more than 10 years and are nurtured by not only our commitment to timely delivery of quality services but also due to our investments and innovations in their technology roadmap. As an organization, we are in an exponential growth phase with a consistent focus on continuous improvement, process-oriented culture, and a true partnership mindset with our customers. We are looking for the right qualified and committed individuals to play an exceptional role as well as to support our accelerated growth.You can learn more about us at: http://www.cesltd.com/Roles & ResponsibilitiesThe AI Engineer is responsible for designing, building, and operationalizing scalable AI/ML solutions across the full lifecycle—from problem framing and data preparation to model development, deployment, and monitoring. This role collaborates closely with multiple teams to deliver measurable impact using machine learning, deep learning, generative AI, and MLOps best practices.
Key Responsibilities
Solution Design & Problem Framing
Translate business challenges into ML problem statements with clear success metrics.
Evaluate and select appropriate algorithms (traditional ML, deep learning, LLMs).
Produce design artifacts: architecture, data flow, risks, and guardrails.
Data Engineering & Feature Development
Build and maintain data pipelines (batch/real-time) using Python, SQL, PySpark.
Engineer reusable features and ensure data quality, validation, and governance.
Work with data owners to define contracts and manage PII responsibly.
Model Development & Evaluation
Develop, train, and tune models using frameworks such as scikit-learn, XGBoost, PyTorch.
Perform evaluation, error analysis, fairness checks, and experiment tracking (MLflow).
Ensure reproducibility, versioning, and documentation.
MLOps & Productionization
Containerize and deploy models using CI/CD pipelines (Azure DevOps, GitHub Actions).
Implement monitoring for model drift, latency, and cost; create auto-retraining workflows.
Optimize inference for scale (quantization, distillation, vector indexing).
Generative AI & LLM Integration
Build Retrieval-Augmented Generation (RAG) workflows: chunking, embeddings, vector stores.
Design prompts, evaluation frameworks, safety guardrails, and hallucination controls.
Assess LLMs using metrics for factuality, consistency, and performance.
Collaboration, Documentation & Leadership
Work cross-functionally with product, engineering, and compliance teams.
Write technical documents, runbooks, model cards, and architecture diagrams.
Mentor junior engineers; participate in technical reviews and hiring.
Required Technical Skills
Languages: Python (proficient), SQL; familiarity with PySpark or Scala a plus.
ML/DL: scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow, MLflow.
Cloud/Platforms: Azure ML, AWS SageMaker, or Databricks.
Data Tools: Pandas, Spark, Delta/Parquet, Great Expectations (or equivalent).
MLOps Stack: Docker, Kubernetes, FastAPI, CI/CD, model registries.
GenAI Stack: LangChain/LlamaIndex (or equivalent agent frameworks), FAISS/pgvector, Azure OpenAI/OpenAI/HF.
Why CES :
Flexible working hours to create a work-life balance.
Opportunity to work on advanced tools and technologies.
Global exposure to not only collaborate with the team, but also to connect with the client portfolio and build professional relationships.
Highly encouraged for any innovative ideas & thoughts and we support in executing the same.
Periodical and on-spot rewards and recognitions on your performance.
Provides a better platform for enhancing skills via many different L&D programs.
Enabling and empowering atmosphere to work along.
