Job Duties:
· Participate in business meetings to gather requirements and translate them into functional specifications, while providing technology consultation to clients and business stakeholders
· Work closely with clients to understand their functional requirements, define project scope, and plan project deliverables.
· Perform analysis and recommends AWS-based solutions tailored to client requirements.
· Engage with stakeholders from the earliest stages of a project through to delivery and go-live.
· Design and implements Salesforce solutions—including custom objects, Apex, Lightning Web Components, and integrations—to streamline CRM workflows and connect customer data with enterprise applications and AI-driven systems.
· Analyze existing systems, assesses evolving business needs, and identifies the most effective approach to migrate legacy applications or develop new ones.
· Provide technical support and guidance by reviewing architecture, design, and project deliverables following Agile methodology.
· Leverage a broad technology stack — including Bootstrap, Web Components, Node.js, React.js, AWS, DevOps, Docker, Kubernetes, and Python — to implement solutions.
· Design and implements agentic AI workflows that autonomously plan, reason, and execute multi-step tasks to automate complex business processes.
· Integrate Large Language Models (LLMs) into enterprise applications, leveraging them for tasks such as content generation, summarization, intelligent search, and natural language understanding.
· Evaluate and selects appropriate LLM models based on use-case requirements, balancing accuracy, latency, and cost considerations.
· Apply prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques to optimize LLM performance for domain-specific needs.
· Build and orchestrates AI agents using modern frameworks and tooling to enable autonomous decision-making and tool invocation within defined guardrails.
· Implement Model Context Protocol (MCP) servers, clients, and custom connectors to securely link LLMs and AI agents with enterprise data sources, tools, and APIs—enabling AI systems to access and act on contextual business data in a standardized, scalable manner across internal systems and third-party platforms.
· Design and develops data engineering and analytics solutions on the Databricks Lakehouse Platform to support AI and machine learning workloads.
· Build an intelligent chatbot on the Databricks Lakehouse Platform by developing scalable data pipelines with Apache Spark and Delta Lake to ingest, transform, and process large datasets, leveraging MLflow for model training, experimentation, deployment, and end-to-end ML lifecycle management, and implementing data governance and collaboration workflows to ensure reliable, high-quality data that powers accurate, context-aware conversational responses.
· Provide technical input and direction to the development team to drive successful delivery.
· Hold accountability and ownership for the quality of delivery.
· Participate in scrum and stakeholder meetings to align on requirements and report progress on ongoing work.
· Conduct and contributes to technical and functional review sessions, supporting the design, implementation, and maintenance of a Digital Marketing Platform within an Agile framework.
· Supervise and manages the team, providing support to ensure timely delivery.
· Conduct unit testing to validate functionality against the defined scope.
· Participate in defect triage calls, analyzes defects, and prioritizes tasks accordingly.
· Deliver the most suitable technical solutions for production defects and enhancements based on priority.
Education: Bachelor''''s degree
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