AI Engineer – 2537

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/

About This Role

We are looking for an experienced AI Engineer to join our team in developing a modular, agent-driven AI platform focused on intelligent automation and support services. You will design and deploy LLM-powered agents, integrate with cloud and SaaS systems, and leverage both open-source and commercial AI models. This role offers the opportunity to work with advanced AI architectures, collaborate across cloud-native infrastructure, and contribute to an innovative, scalable AI ecosystem.

Key Responsibilities
•    Design and implement LLM-based agents for intelligent task execution and automation using OpenAI, Claude (Anthropic), or Ollama models.
•    Build and deploy AI pipelines to process real-time application metrics, logs, and alerts.
•    Develop prompt strategies and agent workflows to enable contextual understanding, memory, and action planning.
•    Integrate AI agents with cloud and SaaS platforms for seamless end-to-end automation.
•    Utilize cloud services on Azure and AWS for model training, serving, and orchestration (e.g., AKS, Lambda, SageMaker).
•    Apply vector search and embeddings to enhance agent reasoning and retrieval-based task support.
•    Collaborate with platform engineers and DevOps to ensure scalable, secure, and maintainable AI deployments.
•    Apply basic ML models for anomaly detection, classification, or time-series trend prediction in monitoring scenarios.
•    Contribute to model evaluation, continuous feedback loops, and runtime optimization
•    Work closely with Full Stack developers to integrate the AI solutions into web applications—must be familiar with frontend-backend integration concepts such as APIs, arrays, JSON, and HTTP protocols.
•    Develop backend APIs using frameworks like FastAPI or Flask to expose AI agent capabilities as services.
•    Candidate should have experience with AI frameworks (e.g., LangChain, Haystack, Semantic Kernel) and use them to build Agent AI solutions.
Requirements

Must-Have:
•    3+ years of experience in AI/ML engineering with a focus on NLP or agent-based systems.
•    Hands-on expertise in Python, with experience in building AI applications and automation workflows.
•    Proficiency with LLMs such as OpenAI (GPT-4), Anthropic Claude, Google Gemini, Ollama (LLaMA models), or similar.
•    Experience working with AI agent frameworks like LangChain, Semantic Kernel, Crew AI, ReAct, or AutoGPT.
•    Familiarity with vector databases (e.g., FAISS, Pinecone, Qdrant) and embeddings-based search.
•    Experience deploying AI models and services on Azure or AWS.
•    Ability to develop and expose AI functionality via backend APIs using frameworks like FastAPI.
•    Basic ML knowledge: experience with libraries like scikit-learn, Prophet, or XGBoost for building or integrating anomaly detection or classification models.
•    Experience applying basic supervised/unsupervised ML models for anomaly detection or prediction.
•    Familiarity with time series forecasting techniques (e.g., ARIMA, Prophet).
•    Understanding of Full Stack development basics, including how APIs interact with frontend systems.
•    Knowledge of monitoring tools (e.g., Datadog, Prometheus, ELK) and data ingestion best practices.
•    Strong understanding of APIs, event-driven architecture, and integration with cloud and SaaS platforms.