agentic systems engineer

Zoolatech

<div><div><div><div><div><div><div><div>OVERVIEW</div>
</div><div><div>RESPONSIBILITIES</div>
</div><div><div>REQUIREMENTS</div>
</div></div></div></div><div><div><div><p>ATRC's Data &amp; AI Office is a product and platform execution team &mdash; not a traditional enterprise data function. We build and operate AI-enabled systems that create measurable operational impact across the organization. Our primary output is working software in production.</p><p><b>
What we are building &mdash; now:</b></p><ul><li><p>
An executive reporting portal serving senior leadership, integrating live data from SAP, Microsoft 365, SuccessFactors, and internal systems across multiple operational views (finance &amp; budget, KPIs, technology portfolio, news, wellbeing, and a personalized executive home)</p></li><li><p>
A sovereign data and AI platform: data warehouse, governed pipelines, metadata catalog, and classification enforcement across all entities</p></li><li><p>
Data mesh architecture with federated ownership &mdash; each entity owns its data products; the platform provides governed access</p></li><li><p>
Data readiness for the organization's agentic AI platform &mdash; clean, structured, governed data so AI agents can operate on trusted sources</p></li><li><p>
Operational AI agents delivering real value on top of the agentic platform (document triage, anomaly detection, automated briefings, classification automation)</p></li><li><p>
AI-enabled situational awareness and crisis management systems</p></li><li><p>
Data governance and classification &mdash; managing an active external vendor engagement, bridging framework deliverables into platform-enforced rules, and building the ongoing</p><p>
governance operations the organization requires</p></li></ul><p><b>
How we work:</b></p><ul><li><p>
Small team, high ownership &mdash; every engineer owns a module end-to-end</p></li><li><p>
Two-week sprints with demonstrated output at every review</p></li><li><p>
Ship and improve &mdash; working systems beat perfect designs</p></li><li><p>
Core team owns all architecture and product decisions; external capacity executes within defined scope</p></li><li><p>
Decisions take hours, not weeks. Daily technical choices are made by the engineer.</p></li><li><p>
Feature scope decisions take 24 hours. Architecture pivots take 48 hours. Nothing sits in limbo.</p></li><li><p>
Oversee 5 other workstreams powered by 3rd party vendors within the ecosystem but whom we are ultimately accountable for</p></li></ul>
</div><div><p>Builds the AI capabilities that operations depend on &mdash; classification automation, production agents, and the data bridge to the agentic platform.</p><p><b>
Mission</b></p><p>
Build, own, and continuously improve the AI capabilities across the office's production systems, working under the direction of the Principal AI Engineer. Your scope: automated data classification (remediating what's currently broken), AI-generated briefings, and operational AI agents that deliver real value in weeks.</p><p>
An external partner is building an agentic platform that ATRC will use. Your role in that relationship: define the data contracts the platform consumes, evaluate the sample agents delivered, and build the production agents on top of the platform that go beyond demonstrations into real operational use. You don't build the platform &mdash; you make sure ATRC's data is ready for it and that what gets built on top of it actually works.</p><p><b>
What This Role Owns</b></p><ul><li><p>
Data classification automation: implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance</p></li><li><p>
Lead Operational AI agents: building production agents on top of the agentic platform &mdash; going beyond the sample agents the external partner delivers into real operational workflows</p></li><li><p>
Agentic platform data contracts: defining what data the platform needs, in what format, with what quality guarantees &mdash; working with the Principal AI Engineer</p></li><li><p>
AI service implementation: FastAPI service around LLM APIs with versioned prompt templates</p></li><li><p>
Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution</p></li><li><p>
Prompt versioning: templates in configuration, editable without code changes</p></li><li><p>
Observability: every LLM call logged with input hash, model version, output, latency, token count</p></li><li><p>
Fallback logic: graceful degradation when LLM APIs are unavailable</p></li><li><p>
Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts</p></li></ul><p><b>
Key Decisions</b></p><ul><li><p>
Prompt implementation &mdash; few-shot examples, output parsing, error handling</p></li><li><p>
Confidence threshold tuning based on evaluation results</p></li><li><p>
Context window packing &mdash; what data fits within token budgets</p></li><li><p>
Fallback behavior when APIs degrade</p></li><li><p>
Agent evaluation &mdash; whether an external partner's sample agent is production-ready or needs rework</p></li><li><p>
Bug triage for AI components &mdash; autonomous fix vs. escalate to Principal AI Engineer</p></li></ul><p><b>
Does Not Do</b></p><ul><li><p>
Define AI architecture or agent design patterns (Principal AI Engineer)</p></li><li><p>
Build the agentic platform itself (external partner)</p></li><li><p>
Build the backend API or data pipelines</p></li><li><p>
Fine-tune or train models</p></li><li><p>
Define governance policies (Governance Lead)</p></li></ul><p><b>
Ideal Candidate</b></p><p>
Has shipped an LLM-based feature that non-technical users depend on daily &mdash; and has been responsible when it breaks. Knows the hardest part of applied AI is the fallback, the observability, and the human review loop &mdash; not the prompt. Can evaluate someone else's agent demo and quickly identify whether it's production-ready or held together with string. Works well under a senior AI lead &mdash; takes architectural direction and executes with high quality and speed. Not attached to a particular model &mdash; the job is reliable output.</p>
</div><div><ul><li><p>LLM APIs (Claude, GPT-4, open-weight models) &mdash; structured output, JSON mode, system prompts</p></li><li><p>
Prompt engineering for classification &mdash; zero-shot and few-shot</p></li><li><p>
Python &mdash; async API calls, retry logic, exponential backoff</p></li><li><p>
LLM evaluation &mdash; precision/recall, human-AI agreement scoring</p></li><li><p>
Structured output &mdash; JSON schema enforcement, Pydantic validation</p></li><li><p>
Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)</p></li><li><p>
Token budgeting and context window management</p></li><li><p>
AI observability &mdash; output quality monitoring, anomaly detection</p></li><li><p>
FastAPI and Docker</p></li></ul>
</div></div></div><div><div><div><div><p>Location:
</p><div>Istanbul, Turkey
</div></div><div><p>Seniority:
</p><div>Senior
</div></div><div><p>Technologies:
</p><div>Python
</div></div></div></div><div><div><p>Benefits:
</p><ul><li><b>Paid Vacation
</b></li><li><b>Hybrid Work (home/office)
</b></li><li><b>Sick Days
</b></li><li><b>Sport/Insurance Compensation
</b></li><li><b>Holidays Day Off
</b></li><li><b>English Classes
</b></li><li><b>Training Compensation
</b></li><li><b>Transportation compensation</b></li></ul></div></div></div></div></div></div>

AI Engineer

C2R Ventures

<div>
 <p>
  Elite multi-strategy hedge fund manager that invests globally in a wide array of assets and strategies, is seeking an elite Agentic AI Research Engineer to play a key role in the ongoing development and enhancement of their investment process.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Do not need financial experience to apply, but of course it is welcome. Candidates from AI research arms/divisions of notable bigtech companies and/or AI start-ups with elite VC backing are very welcome to apply.
 </p>
 <p>
  <br/>
 </p>
 <p>
  This Engineer will be embedded in investment teams to augment their investment process through designing and building multi-agent frameworks by developing deep understanding of each business and leveraging cutting-edge methods.
 </p>
 <p>
  <br/>
 </p>
 <p>
  This is a Full Stack role that will encompass the building of foundational infrastructure and developing robust data pipelines through agentic design.
 </p>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Responsibilities:
  </strong>
 </p>
 <ul>
  <li>
   Work with investment teams to find solutions to their most vexing challenges, applying agents to assist in solving problems
  </li>
  <li>
   Design, build, and integrate a multi-agent AI framework that can orchestrate deep research in and across vastly different subject matter and domain expertise
  </li>
  <li>
   Implement rigorous quantitative benchmarks for large scale agentic tasks
  </li>
  <li>
   Assist with automated evaluation of AI models and prompts across a wide variety of project and investment teams
  </li>
  <li>
   Help create and optimize data mixes for model training that maximize agent performance or ease of use on agentic tasks
  </li>
 </ul>
 <p>
  <br/>
 </p>
 <p>
  <strong>
   Requirements:
  </strong>
 </p>
 <ul>
  <li>
   0&gt;1 builder who is comfortable with uncertainty, operates with high agency, and excels at rapid iteration and shipping
  </li>
  <li>
   Experience developing complex agentic systems using LLMs with an emphasis on agentic harness design (e.g., communication architectures for agents)
  </li>
  <li>
   Successfully demonstrated the ability of taking a product from conception to completion
  </li>
  <li>
   Understanding of LLM-as-judge, reward hacking, specification gaming robustness problems
  </li>
  <li>
   Have strong Python skills and can build robust infrastructure across the stack
  </li>
 </ul>
</div>


Artificial Intelligence Engineer

Archer Integrated Risk Management

<div>
 Job Details
 <br/>
 <br/>
 <strong>
  Description
  <br/>
  <br/>
 </strong>
 FULL-STACK DATA ENGINEER II
 <br/>
 <br/>
 Location: Cairo, Egypt
 <br/>
 <br/>
 Experience: 2&ndash;4 Years
 <br/>
 <br/>
 Stack: DataBricks &amp; Agentic AI
 <br/>
 <br/>
 The Role
 <br/>
 <br/>
 We are looking for a forward-thinking Full-Stack Data Engineer II to join our team in Egypt. This is not a traditional ETL role; you will be at the forefront of the Data + AI era, leveraging DataBricks to build intelligent data products. You will be responsible for the end-to-end lifecycle of data, from ingestion and Lakehouse architecture to developing agentic AI workflows that allow our business systems to act autonomously based on data insights.
 <br/>
 <br/>
 AI &amp; Agentic Focus
 <br/>
 <br/>
 Build and deploy LLM-based agents that utilize data tools to solve complex tasks, including implementing Retrieval-Augmented Generation (RAG) patterns and autonomous chains-of-thought for decision-making.
 <br/>
 <br/>
 Key Responsibilities
 <br/>
 <br/>
 <ul>
  <li>
   Design and optimize Delta Lake tables within DataBricks using Unity Catalog for governance.
  </li>
  <li>
   Develop and deploy agentic workflows using LangChain, AutoGen, or DataBricks Mosaic AI.
  </li>
  <li>
   Build Python-based backend services (FastAPI) to expose AI-driven insights.
  </li>
  <li>
   Manage data pipelines using DataBricks Workflows and Delta Live Tables (DLT).
  </li>
  <li>
   Implement monitoring frameworks to ensure agent accuracy, safety, and performance.
   <br/>
   <br/>
  </li>
 </ul>
 Technical Requirements
 <br/>
 <br/>
 <ul>
  <li>
   2&ndash;4 years experience with PySpark and SQL
  </li>
  <li>
   Strong experience with DataBricks (Unity Catalog, DLT, Notebooks)
  </li>
  <li>
   Understanding of Lakehouse architecture
  </li>
  <li>
   Hands-on experience with LLMs and prompt engineering
  </li>
  <li>
   Experience building AI agents using APIs and tools
  </li>
  <li>
   Backend development in Python and Docker
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Experience
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   MLflow for model tracking and deployment
  </li>
  <li>
   Vector databases (Pinecone, Weaviate, DataBricks Vector Search)
  </li>
  <li>
   Familiarity with Egyptian data localization requirements
   <br/>
   <br/>
  </li>
 </ul>
 What We Offer
 <br/>
 <br/>
 <ul>
  <li>
   Work on cutting-edge Agentic AI projects
  </li>
  <li>
   Competitive compensation in EGP with bonuses
  </li>
  <li>
   Hybrid work environment in New Cairo
   <br/>
   <br/>
  </li>
 </ul>
 Updated Version: DataBricks &amp; Agentic AI Focused &ndash; Egypt
</div>


Full-stack Data Engineer II

Luxoft

<div><div><h5 class="jobSectionHeader"><b>Project description
</b></h5><div>The paradigm of software is shifting from reactive to proactive. At Luxoft, we are at the forefront of this transformation, engineering the next generation of Agentic AI systems. We are seeking an exceptional Backend Engineer to architect the core infrastructure for software that doesn't just execute commands, but reasons, plans, and acts autonomously.<br>
<br>
As a key member of our elite AI Core team, you will move beyond traditional backend development to build the central nervous system for our AI agents. This role is for a true systems builder&mdash;someone passionate about creating robust, scalable, and intelligent platforms that empower autonomous AI to solve complex, real-world problems. You will be instrumental in crafting the bridge between raw data and digital cognition.<br>
<br>
What You'll Be Building: The Engine of Intelligence<br>
<br>
As our Backend Engineer for Agentic Systems, you will own the end-to-end lifecycle of the agent's core infrastructure&mdash;from its memory and logic to its deployment and operational resilience.<br>
<br>
<ul><li>The Agent's Communication Backbone: You will wield FastAPI and Pydantic to design and build high-throughput, asynchronous, and self-documenting APIs. These are more than just endpoints; they are the high-bandwidth channels through which our agents perceive data, communicate their intent, and interact with external tools and systems. Your Pydantic models will define the rigorous data contracts that ensure absolute integrity across our distributed environment.</li></ul><br>
<ul><li>The Agent's Cognitive Framework: You will architect and implement the core systems that enable agentic behavior. This involves developing sophisticated state machines, implementing advanced reasoning loops (e.g., ReAct, Chain-of-Thought), and orchestrating the workflows that allow agents to plan, delegate tasks, and leverage a diverse suite of tools.</li></ul><br>
<ul><li>The Agent's Memory and Knowledge Base: An agent's power lies in its ability to learn and recall. You will be the steward of our agents' memory systems, leveraging the reliability of PostgreSQL. You will design and manage complex database schemas for storing conversation histories, learned knowledge graphs, and operational logs. Your expertise in database management and optimization will be critical to ensuring our agents can access information instantaneously as their knowledge scales.</li></ul><br>
<ul><li>The Agent's Operational Habitat: Our AI agents must be deployed with unwavering reliability and scale on demand. You will master Docker to containerize our entire agentic ecosystem, creating reproducible, isolated, and portable environments that guarantee consistency from development to production.
</li></ul></div></div><div><h5 class="jobSectionHeader"><b>Responsibilities</b></h5>
<div><div><p>Architect &amp; Develop: Design, build, and maintain high-performance backend services in Python, using FastAPI for our core agentic platform.
</p></div><div><p>Model Reality: Implement complex data validation and serialization schemas with Pydantic to ensure data integrity across our distributed systems.
</p></div><div><p>Empower Agents: Develop the core logic for AI agents, including tool integration, state management, and multi-step reasoning frameworks.
</p></div><div><p>Manage Data Persistence: Design, optimize, and manage PostgreSQL databases. Write efficient SQL queries, manage migrations, and ensure the long-term health and scalability of our data stores.
</p></div><div><p>Containerize &amp; Deploy: Create and maintain clean, efficient Dockerfiles and Docker Compose configurations for all backend services, streamlining our deployment pipelines.
</p></div><div><p>Collaborate &amp; Innovate: Work in close partnership with AI Researchers, Prompt Engineers, and Frontend Developers to deliver cohesive, end-to-end intelligent solutions.
</p></div><div><p>Test &amp; Harden: Author comprehensive unit and integration tests to guarantee the reliability and robustness of the foundational systems powering our agents.
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Skills</b></h5>
<p>Must have
</p><div><div><p>Technical Excellence:
</p></div><div><p>Proven Professional Experience: A minimum of 3 years in backend engineering, with a strong portfolio of building scalable, production-grade systems using robust Python skills. This includes deep, hands-on experience with database management.
</p></div><div><p>Deep expertise in FastAPI and a strong appreciation for its asynchronous capabilities.
</p></div><div><p>Mastery of Pydantic for creating strict, type-safe data models that enforce architectural clarity.
</p></div><div><p>Experince in any of common agentic frameworks such as langGraph, Pydantic-AI, Google ADK, Agno or any similar framework
</p></div><div><p>Strong command of PostgreSQL, including advanced schema design, query optimization, and indexing strategies.
</p></div><div><p>Production-level experience with Docker, with a proven ability to write clean, multi-stage Dockerfiles and manage containerized applications.
</p></div><div><p>The Agentic Mindset:
</p></div><div><p>A profound passion for AI and a demonstrable interest in agentic systems. You have likely experimented with frameworks like LangChain, LlamaIndex, or AutoGen, or you possess a strong desire to build such systems from fundamental principles.
</p></div><div><p>A systems-thinking approach. You can visualize how disparate components&mdash;LLMs, databases, APIs, and tools&mdash;integrate to form a single, intelligent entity.
</p></div><div><p>Personal Attributes:
</p></div><div><p>Insatiable Curiosity: You are constantly learning and exploring the bleeding edge of AI and software engineering.
</p></div><div><p>Ownership Mentality: You don't just write code; you take ownership of the systems you build and are accountable for their performance and reliability.
</p></div><div><p>Collaborative Spirit: You thrive in a team environment, sharing knowledge and elevating those around you.
</p></div></div><p>Nice to have
</p><div><div><p>Experience with vector databases (e.g., Pinecone, Chroma, Weaviate).
</p></div><div><p>Knowledge of cloud platforms (AWS, GCP, Azure) and infrastructure-as-code (Terraform)
</p></div><div><p>Familiarity with CI/CD pipelines (e.g., GitHub Actions, GitLab CI)
</p></div><div><p>Experience with message queues (e.g., RabbitMQ, Kafka) for asynchronous task processing.
</p></div></div></div><div><h5 class="jobSectionHeader"><b>Other</b></h5>
<div><div><div><p>Languages</p>
<p>English: B2 Upper Intermediate
</p></div></div><div><div><p>Seniority</p>
<p>Senior</p></div></div></div></div></div>
<br><div><div><div><p>Cairo, Egypt
</p></div><p>Req. VR-121738
</p></div><div></div><div><div><p>AI/ML
</p></div><div><p>Automotive Industry</p>
</div><div><p>27/04/2026
</p></div><div><p>Req. VR-121738</p></div></div></div><p></p>

Backend Engineer for AI Agentic Systems

ZENCLOUD TECHNOLOGIES (PRIVATE) LIMITED

<p>We are looking for a talented and motivated <b>Full Stack Engineer</b> with <b>1&ndash;2 years of experience</b> to join our growing team. The ideal candidate should have strong hands-on experience in the <b>MERN Stack (MongoDB, Express.js, React.js, Node.js)</b> along with practical knowledge of <b>Python</b> and <b>Agentic AI systems</b> for building intelligent, scalable, and modern applications.</p><p><b>Key Responsibilities:</b></p><ul><li>Develop and maintain full stack web applications using the MERN Stack.</li><li>Build responsive and user-friendly front-end interfaces using React.js.</li><li>Design and develop scalable back-end services and REST APIs using Node.js / Express.js.</li><li>Manage and optimize MongoDB databases.</li><li>Develop AI-powered features, workflows, and automation using Python.</li><li>Build and integrate <b>Agentic AI solutions</b>, LLM-based tools, AI assistants, and autonomous workflows.</li><li>Collaborate with cross-functional teams to implement innovative features.</li><li>Troubleshoot, debug, and optimize system performance.</li><li>Write clean, maintainable, and efficient code following best practices.</li></ul><p><b>Required Skills &amp; Qualifications:</b></p><ul><li><b>1&ndash;2 years of professional experience</b> in Full Stack Development.</li><li>Strong hands-on experience with <b>MongoDB, Express.js, React.js, and Node.js</b>.</li><li>Proficiency in <b>Python</b> for backend development, automation, or AI solutions.</li><li>Experience with <b>Agentic AI</b>, AI agents, LLM integrations, or workflow automation tools.</li><li>Solid knowledge of <b>JavaScript, HTML5, CSS3</b>.</li><li>Experience with REST APIs and third-party integrations.</li><li>Familiarity with Git/version control systems.</li><li>Strong problem-solving and communication skills.</li></ul><p>Work Location: In person</p>

Full Stack Engineer (MERN Stack + AI/Python)

<div>
 <p>
  <strong>
   Job Title: Agentic AI Data Engineer
  </strong>
 </p>
 <p>
  <strong>
   Hybrid: Jersey City,NJ
  </strong>
 </p>
 <p>
  <strong>
   Job Type: FullTime
  </strong>
 </p>
 <p>
  <strong>
   Max Budget : $150K
  </strong>
 </p>
 <p>
  <strong>
   Total Experience required : 5-10 Years
  </strong>
 </p>
 <p>
  <br/>
 </p>
 <p>
  Role Overview
 </p>
 <p>
  We are seeking a highly skilled Agentic AI Data Engineer to design, build, and optimize intelligent, autonomous data systems that power next-generation AI applications. This role blends data engineering, machine learning infrastructure, and emerging agent-based AI frameworks to enable scalable, self-orchestrating pipelines and decision-making systems.
 </p>
 <p>
  You will work at the intersection of data platforms, large language models (LLMs), and cloud-native architectures&mdash;building systems that can reason, act, and adapt autonomously.
 </p>
 <p>
  ---
 </p>
 <p>
  Key Responsibilities
 </p>
 <p>
  &middot; Design and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines
 </p>
 <p>
  &middot; Build scalable data pipelines for structured and unstructured data (batch + real-time)
 </p>
 <p>
  &middot; Develop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks
 </p>
 <p>
  &middot; Integrate AWS AI/ML services into production-grade architectures
 </p>
 <p>
  &middot; Develop and optimize data lakes, warehouses, and lakehouse architectures
 </p>
 <p>
  &middot; Build APIs and microservices to expose AI/ML capabilities
 </p>
 <p>
  &middot; Ensure data quality, governance, and security across pipelines
 </p>
 <p>
  &middot; Collaborate with data scientists, ML engineers, and product teams to deploy AI solutions
 </p>
 <p>
  &middot; Implement monitoring, logging, and observability for AI agents and pipelines
 </p>
 <p>
  &middot; Optimize cost and performance of cloud-based AI workloads
 </p>
 <p>
  ---
 </p>
 <p>
  Required Technical Skills
 </p>
 <p>
  Cloud &amp; AWS Ecosystem
 </p>
 <p>
  &middot; Strong experience with AWS services, including:
 </p>
 <p>
  o Amazon S3, Glue, Lambda, Step Functions
 </p>
 <p>
  o Amazon Redshift / Athena
 </p>
 <p>
  o Amazon SageMaker (training, deployment, pipelines)
 </p>
 <p>
  o Amazon Bedrock (foundation models, agents, knowledge bases)
 </p>
 <p>
  AI/ML &amp; Agentic Systems
 </p>
 <p>
  &middot; Experience with LLMs and generative AI systems
 </p>
 <p>
  &middot; Hands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems)
 </p>
 <p>
  &middot; Familiarity with AgentCore / agent orchestration platforms
 </p>
 <p>
  &middot; Understanding of RAG architectures, embeddings, and vector databases
 </p>
 <p>
  &middot; Experience with model deployment, inference optimization, and prompt engineering
 </p>
 <p>
  Data Engineering
 </p>
 <p>
  &middot; Strong proficiency in Python and SQL
 </p>
 <p>
  &middot; Experience with ETL/ELT tools and frameworks
 </p>
 <p>
  &middot; Distributed data processing (Spark, PySpark, or similar)
 </p>
 <p>
  &middot; Streaming technologies (Kafka, Kinesis, or similar)
 </p>
 <p>
  &middot; Data modeling and schema design
 </p>
 <p>
  Data &amp; AI Infrastructure
 </p>
 <p>
  &middot; Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch)
 </p>
 <p>
  &middot; Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi)
 </p>
 <p>
  &middot; Containerization (Docker) and orchestration (Kubernetes)
 </p>
 <p>
  &middot; CI/CD for ML and data pipelines
 </p>
 <p>
  ---
 </p>
 <p>
  Preferred Qualifications
 </p>
 <p>
  &middot; Experience building autonomous AI agents for enterprise use cases
 </p>
 <p>
  &middot; Knowledge of multi-agent collaboration systems and planning algorithms
 </p>
 <p>
  &middot; Familiarity with LangChain, LlamaIndex, or similar frameworks
 </p>
 <p>
  &middot; Experience with MLOps and LLMOps practices
 </p>
 <p>
  &middot; Understanding of graph-based workflows and knowledge graphs
 </p>
 <p>
  &middot; Exposure to real-time AI systems and event-driven architectures
 </p>
 <p>
  ---
 </p>
 <p>
  Soft Skills
 </p>
 <p>
  &middot; Strong problem-solving and system design skills
 </p>
 <p>
  &middot; Ability to work in fast-paced, evolving AI environments
 </p>
 <p>
  &middot; Effective communication and cross-functional collaboration
 </p>
 <p>
  &middot; Curiosity and adaptability to emerging AI technologies
 </p>
 <p>
  ---
 </p>
 <p>
  Education &amp; Experience
 </p>
 <p>
  &middot; Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Engineering, or related field
 </p>
 <p>
  &middot; 4+ years of experience in data engineering or ML engineering
 </p>
 <p>
  &middot; Hands-on experience with production-grade AI/ML systems
 </p>
 <p>
  ---
 </p>
 <p>
  Nice-to-Have
 </p>
 <p>
  &middot; Experience with reinforcement learning or planning systems
 </p>
 <p>
  &middot; Background in distributed systems design
 </p>
 <p>
  &middot; Contributions to open-source AI/data projects
 </p>
 <p>
  &middot; Certifications in AWS (e.g., Solutions Architect, Machine Learning Specialty)
 </p>
 <p>
  ---
 </p>
 <p>
  What You&rsquo;ll Build
 </p>
 <p>
  &middot; Autonomous data pipelines that self-heal and optimize
 </p>
 <p>
  &middot; AI agents capable of reasoning over enterprise data
 </p>
 <p>
  &middot; Scalable LLM-powered applications integrated with business workflows
 </p>
 <p>
  &middot; Intelligent systems that move beyond automation into decision-making
 </p>
 <p>
  <br/>
 </p>
 <p>
  <br/>
 </p>
 <p>
  For more information on benefits and what we offer please visit us at
  <u>
   https://www.exlservice.com/us-careers-and-benefits
  </u>
 </p>
</div>


Agentic AI Data Engineer

NorthBay - Pakistan

<div>
 <strong>
  Role: Team Lead - Azure AI (Agentic) Engineer
  <br/>
  <br/>
 </strong>
 <strong>
  Location:
 </strong>
 Pakistan (hybrid work model)
 <br/>
 <br/>
 <strong>
  <strong>
   Client: Middle East (Government)
   <br/>
   <br/>
  </strong>
 </strong>
 <strong>
  About The Role
  <br/>
  <br/>
 </strong>
 NorthBay Solutions, an AWS global Consulting Partner with distributed teams across the US, Canada, UAE, India, and Pakistan, is hiring a high-caliber
 <strong>
  Azure AI Specialist (Team Lead)
 </strong>
 with deep expertise in building enterprise-grade
 <strong>
  Generative AI, RAG, and Agentic AI solutions
 </strong>
 on the Azure ecosystem.
 <br/>
 <br/>
 This role requires hands-on experience in designing scalable, multi-tenant AI systems, integrating LLMs, and building intelligent pipelines using Azure-native services.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Design and deploy LLM-powered applications using Azure OpenAI for enterprise use cases
  </li>
  <li>
   Build and optimize Retrieval-Augmented Generation (RAG) pipelines using Azure AI Search (vector + hybrid search)
  </li>
  <li>
   Develop Agentic AI systems leveraging frameworks and tool-based reasoning approaches
  </li>
  <li>
   Implement custom Document Intelligence models for high-accuracy data extraction (e.g., IDs, structured documents)
  </li>
  <li>
   Architect multi-tenant AI deployments with scalability, security, and cost-efficiency in mind
  </li>
  <li>
   Develop and manage Knowledge Graphs for structured reasoning and contextual intelligence
  </li>
  <li>
   Apply advanced prompt engineering techniques (Few-shot, Chain-of-Thought, ReAct)
  </li>
  <li>
   Use Prompt Flow in Azure AI Studio for experimentation, testing, and versioning
  </li>
  <li>
   Design intelligent conversational systems with fallback handling and contextual memory
  </li>
  <li>
   Build and manage MLOps pipelines using Azure Machine Learning
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Skills &amp; Experience
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Overall 10+ years experience, with a focus on Azure OpenAI API (deployments, scaling, optimization)
  </li>
  <li>
   Proven expertise in building RAG pipelines using Azure AI Search
  </li>
  <li>
   Experience with vector databases and hybrid search architectures
  </li>
  <li>
   Practical experience in Agentic AI / multi-agent systems
  </li>
  <li>
   Hands-on with Azure AI Document Intelligence (Form Recognizer)
  </li>
  <li>
   Solid understanding of Knowledge Graphs and semantic modeling
  </li>
  <li>
   Expertise in prompt engineering &amp; LLM optimization techniques
  </li>
  <li>
   Experience with Prompt Flow (Azure AI Studio)
  </li>
  <li>
   Strong background in Python and cloud-native development
  </li>
  <li>
   Experience with Azure ML / MLOps pipelines
  </li>
  <li>
   Ability to design scalable and secure enterprise AI architectures
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice to Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with LangChain, Semantic Kernel, or similar frameworks
  </li>
  <li>
   Exposure to multi-agent orchestration tools
  </li>
  <li>
   Prior experience working with enterprise clients or large-scale AI deployments
  </li>
 </ul>
</div>


Azure AI (Agentic) Specialist - Team Lead

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