Software Engr II

We are seeking a highly skilled AI Engineer to design, build, deploy, and scale next-generation Generative AI and Agentic AI solutions for enterprise applications. The ideal candidate will have deep expertise in Large Language Models (LLMs), Retrieval Augmented Generation (RAG), agentic frameworks, and AI-driven workflow automation. This role will contribute to the development of intelligent AI agents capable of reasoning, planning, collaborating, and operating within complex enterprise environments.

Your role will also include overseeing, supervising and reviewing tasks performed by team members to ensure effective execution of work; managing end-to-end processes and projects for both internal and external clients with responsibility for timely and accurate delivery; issuing clear instructions and directions to team members on tasks to be performed; and mentoring and guiding junior colleagues to support their skill development, professional growth, and overall success

Preferred Qualifications

• Experience delivering AI solutions in regulated enterprise environments.
• Familiarity with AI governance, security, privacy, and responsible AI practices.
• Experience building multi-agent ecosystems for operational or business workflows.
• Understanding of model evaluation, observability, and AI performance monitoring.
• Exposure to MLOps and operationalization of AI workloads.

Education

Bachelors or masters degree in computer science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline.

• Design and develop production-grade Generative AI and Agentic AI applications.
• Build autonomous and semi-autonomous AI agents that can reason, plan, use tools, and collaborate with other agents.
• Develop advanced RAG pipelines leveraging vector databases, embeddings, and enterprise data sources.
• Create multi-step reasoning workflows combining deterministic business logic with LLM-generated reasoning.
• Implement short-term and long-term memory architectures for persistent contextual interactions.
• Design and maintain resilient AI workflows incorporating self-correction, failure recovery, debugging loops, and Human-in-the-Loop (HITL) controls.
• Integrate LLMs, VLMs, enterprise APIs, databases, and external tools into agent ecosystems.
• Deploy and manage AI applications across cloud, hybrid, and on-premises environments.
• Collaborate with product, engineering, and domain experts to translate business requirements into scalable AI solutions.
• Ensure production readiness through monitoring, testing, performance optimization, and governance controls.

Required Technical Skills & Experience

Programming & Software Engineering

• Strong proficiency in Python.
• Experience designing and implementing enterprise-grade software solutions.
• Familiarity with software engineering best practices, version control, testing, and CI/CD pipelines.

Generative AI & Agentic AI

• Hands-on production experience with agent orchestration frameworks such as LangGraph, OpenAI Agents SDK, or equivalent.
• Deep understanding of foundational agentic design patterns, including:
  • Reflection
  • Tool Use
  • Planning
  • Multi-Agent Collaboration
• Practical experience with:
  • Function calling
  • Structured outputs
  • Prompt engineering
  • Context window management

LLMs & Foundation Models

• Experience building applications using:
  • Large Language Models (LLMs)
  • Vision Language Models (VLMs)
  • Embedding models
• Strong understanding of model capabilities, limitations, and optimization techniques.

Retrieval-Augmented Generation (RAG)

• Experience designing and implementing advanced RAG architectures.
• Hands-on experience with:
  • Vector databases
  • Embeddings
  • Semantic search
  • Enterprise knowledge retrieval systems

AI Workflow Orchestration

• Proven ability to develop:
  • Self-healing workflows
  • Automated debugging mechanisms
  • Human-in-the-Loop approval processes
  • Quality assurance and audit checkpoints

Cloud & Deployment

• Experience deploying Generative AI solutions in:
  • On-premises environments
  • Hybrid architectures
  • Public cloud environments
• Hands-on experience with cloud platforms such as:
  • Microsoft Azure
  • AWS
  • Google Cloud Platform (GCP)

Infrastructure & Platforms

• Experience with:
  • Docker
  • Kubernetes
  • Containerized deployments
• Understanding of scalable AI infrastructure and distributed systems.

Data Science & Machine Learning

• Experience applying data mining and machine learning techniques, including:
  • Classification
  • Regression
  • Clustering
  • Decision Trees
  • Neural Networks
  • Support Vector Machines (SVM)
  • Anomaly Detection
  • Recommender Systems
  • Pattern Discovery
  • Text Mining
• Knowledge of statistical modeling and predictive analytics.

Integration & Enterprise Systems

• Experience integrating AI solutions with:
  • Enterprise APIs
  • Databases
  • Internal and external data sources
  • Business applications and workflows

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.