Assistant Manager IT

Key Responsibilities Design and develop Agentic AI systems (multi-step reasoning tool use orchestration) that integrate natively into client applications and workflows. Build fine-tune and evaluate ML/LLM models using established ML training platforms and pipelines. Collaborate directly with client stakeholders and engineering teams to gather requirements scope solutions and translate business problems into AI-driven technical designs. Integrate agentic workflows with client-native applications via APIs SDKs and platform-specific tooling (web mobile or enterprise systems). Implement RAG pipelines vector databases and prompt/context engineering strategies to improve agent accuracy and reliability. Own model/agent evaluation defining metrics running experiments and iterating based on performance and client feedback. Ensure solutions are production-ready: focus on scalability latency observability cost and security of deployed AI systems. Work cross-functionally with data engineering product and QA teams to ship reliable well-tested AI features. Document architecture model behavior and integration patterns for internal teams and client handover. Stay current with the agentic AI and ML tooling landscape and recommend adoption of relevant frameworks and Skills & Experience 35 years of overall engineering experience with hands-on experience building AI/ML-powered applications. Practical experience building Agentic AI systems using frameworks such as LangChain LangGraph AutoGen CrewAI or equivalent. Experience integrating AI/ML capabilities into client-native applications (web mobile or enterprise software) via APIs and SDKs. Working knowledge of ML training platforms (e.g. SageMaker Vertex AI Azure ML or similar) for model training fine-tuning and deployment. Strong programming skills in Python; familiarity with JavaScript/TypeScript or a mobile/native stack is a plus for client-application integration. Solid understanding of LLM fundamentals: prompt engineering embeddings RAG vector databases (e.g. Pinecone FAISS Weaviate). Experience with cloud platforms (AWS Azure or GCP) and containerized deployment (Docker/Kubernetes). Familiarity with MLOps practices model versioning CI/CD for ML monitoring and observability. Strong client-facing communication skills; comfortable presenting technical solutions to non-technical stakeholders. Prior experience in an IT services/consulting environment working across multiple concurrent client engagements is highly preferred.

Qualifications :

Bachelors or Masters degree in Computer Science Engineering Data Science or a related field.

Remote Work :

No

Employment Type :

Full-time