agentic ai engineer

FocusKPI Inc.

<div><p>We are seeking an <b>Agentic AI Engineer</b> to support the development of a customized AI agent solution for one of our clients.<br>
<br>
We're a consulting team that helps clients design and build customized AI agent solutions. You'll work across engagements &mdash; understanding client problems, architecting the right agentic approach, and shipping working systems. This is not a research role. You'll be in the work &mdash; building agents that go live, with real clients, on real timelines.<br>
<br>
<b>Environment:</b><br>
<br>
We move fast and work across multiple client contexts. You'll need to be comfortable with ambiguity, adapt quickly across industries, and take ownership of your engagements without much hand-holding.<br>
<br>
If you thrive in structured, single-product environments with long planning cycles, this probably isn't the right fit. If you love the variety of consulting and want to build things that actually ship, let's talk.<br>
<br>
<b>Work Location:</b><b> </b>Remote position (40 hours per week)<br>
<b>Duration:</b> Full-time role<br>
<b>Pay-rate:</b> $30+/hr<br>
<br>
<b>**No C2C resumes are considered**</b><br>
<br>
<b>Responsibilities:</b></p><ul><li>
Consult with clients to understand their workflows, pain points, and where agentic AI can create real leverage</li><li>
Design and build custom AI agent solutions tailored to each client's stack, data, and constraints</li><li>
Own full delivery &mdash; from architecture and prompt design to tool integration, testing, and handoff</li><li>
Build evals and monitoring so clients can trust what's running in production</li><li>
Move across engagements &mdash; you'll context-switch between clients and industries</li></ul><p><b>
Qualifications:</b></p><ul><li>
A minimum of <b>2 years</b> of experience is required</li><li><b>
Education requirements:</b> Master's degree</li><li><b>
Mandatory skills:</b> Agentic AI, Claude, LLM</li><li>
Building production-grade agentic systems using frameworks like LangChain, LangGraph, CrewAI, AutoGen, or Claude Agent SDK</li><li>
Designing agent architectures: tool routing, memory, multi-agent coordination, retrieval pipelines</li><li>
Translating a client's messy real-world process into a structured agent design</li><li>
Communicating clearly with non-technical stakeholders &mdash; you can explain what you're building and why</li><li>
Delivering under real project constraints &mdash; timelines, client expectations, shifting requirements</li></ul><p><b>
Nice to have:</b></p><ul><li>
Experience building agents for GTM use cases &mdash; sales research, outreach automation, CRM workflows, lead enrichment, or marketing operations</li><li>
Comfort connecting agents to third-party APIs, CRMs (HubSpot, Salesforce), or communication tools</li></ul><p></p><p>
Thank you!</p><p>
FocusKPI Hiring Team</p><p>
Founded in 2010, FocusKPI, Inc. (FocusKPI) is a data science and technology firm specializing in predictive analytics practice and methodologies. FocusKPI is a US company headquartered in Silicon Valley, California, with an East Coast office in Boston, Massachusetts.</p><p>
</p><p><b>NOTICE: </b>Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .<br>
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.</p><p>
</p><p>DC4HSZaJiV</p></div>

Agentic AI Engineer

Tata Consultancy Services (TCS)

<div></div><div><div><div><div><div>Must Have Technical/Functional Skills
</div> <div>GEN AI &amp; AGENTIC AI build &amp; Implementation experience
</div><ul><li>Hands-on experience with LLMs and RAG architectures
</li></ul><ul><li>Experience with vector databases and semantic search
</li></ul><ul><li>Practical understanding of prompt engineering and evaluation
</li></ul><ul><li>Hands-on experience building agentic or multi-step AI workflows (not just prompting)
</li></ul><ul><li>Experience with orchestration frameworks and tool calling
</li></ul><ul><li>Understanding of AI safety, guardrails, and reliability patterns
</li></ul><ul><li>Ability to operationalize AI agents at enterprise scale
</li></ul><div>Roles &amp; Responsibilities
</div> <ul><li>Build end-to-end GenAI applications using LLMs (text, documents, Q&amp;A, summarization, extraction)
</li></ul><ul><li>Implement RAG pipelines: data ingestion, chunking, embeddings, vector search, and grounded responses
</li></ul><ul><li>Develop APIs and microservices to integrate GenAI solutions with enterprise systems
</li></ul><ul><li>Implement structured outputs, function calling, and prompt frameworks
</li></ul><ul><li>Support evaluation, testing, monitoring, and continuous improvement of GenAI solutions
</li></ul><ul><li>Collaborate closely with the GenAI Architect and Agentic AI Engineers
</li></ul><ul><li>Design and implement agentic AI workflows (planning execution observation iteration)
</li></ul><ul><li>Build tool-using AI agents integrated with APIs, data sources, and enterprise systems
</li></ul><ul><li>Implement multi-agent or orchestrated agent architectures where required
</li></ul><ul><li>Combine Agentic AI with RAG for grounded, enterprise-safe responses
</li></ul><ul><li>Implement guardrails, safety checks, and evaluation mechanisms
</li></ul><ul><li>Debug, optimize, and operationalize agentic workflows in production</li></ul><div></div><div><br>
</div>
<div></div><br>
<br>
<div>Salary Range: $130,000 to $170,000 per year
</div></div></div></div><div><div><div><b>Location</b></div>
<div>New York, NY</div>
</div><div><div><b>Job Function</b></div>
<div>TECHNOLOGY</div>
</div><div><div><b>Role</b></div>
<div>Engineer</div>
</div><div><div><b>Job Id</b></div>
<div>406637</div>
</div><div><div><b>Desired Skills</b></div>
<div>Artificial Intelligence</div>
</div><div><div><b>Salary Range</b></div>
<div>$130,000-$170,000 a year</div>
</div></div><p>Desired Candidate Profile
</p><div><div><b>Qualifications</b> : BACHELOR OF COMPUTER SCIENCE</div></div></div><div></div>

GEN AI and AGENTIC AI Engineer

VeeRteq Solutions Inc.

<div><p>We are seeking a high-caliber <b>AI Tech Lead / AI Engineer</b> to design, build, and operationalize agentic AI solutions that accelerate analytics and reporting delivery. This role goes beyond basic agent creation and requires hands-on experience with agent orchestration, memory, and enterprise-grade data access patterns, delivering scalable components that fit into an existing framework while rapidly adapting to incoming business requests.</p>
<p></p><p><b>Responsibilities: -</b></p>
<ul><li>Design, Architect, implement, and iterate on agentic AI solutions to shorten the cycle time for analytics, log analysis, and reporting requests.</li>
<li>Design agent orchestration patterns (multi-agent workflows), tool/function calling, and memory approaches appropriate for enterprise deployments.</li>
<li>Define secure and scalable data access patterns for agents (retrieval, context building, and grounding), integrating with existing data sources and governance expectations.</li>
<li>Partner closely with product, analytics, and engineering stakeholders to intake requirements quickly and deliver working prototypes and production-ready solutions.</li>
<li>Engineer reusable components and best practices that enable scalable delivery (not one-off scripts), aligned to an existing base framework.</li>
<li>Operationalize solutions for reliability and maintainability: testing strategies, monitoring/observability, prompt/version management, and deployment automation.</li>
<li>Evaluate build vs. buy options pragmatically when needed, while keeping focus on shipping solutions on the current platform stack.</li>
</ul><p></p><p><b>Tech Stack (Core): -</b></p>
<ul><li><b>Cloud:</b> AWS (primary deployment environment; open to alternatives)</li>
<li><b>Data/Analytics Platform:</b> Databricks (including native &quot;chat with data&quot; capabilities and potential agent integrations)</li>
<li><b>Agent Frameworks:</b> LangChain, LangGraph</li>
<li><b>Conversational analytics patterns:</b> Ask-questions-on-data / conversational BI approaches (agent-driven analytics and dashboards</li>
</ul><p></p><p><b>Experience: -</b></p>
<p>10+ Years</p>
<p></p><p><b>Location: -</b></p>
<p>Rosemont, IL (3days/Week)</p>
<p></p><p><b>Educational Qualifications: -</b></p>
<p>Engineering Degree BE/ME/BTech/MTech/BSc/MSc.</p>
<p>Technical certification in multiple technologies is desirable.</p>
<p><b>Skills: -</b></p>
<p><b>Mandatory skills</b></p>
<ul><li>Demonstrated experience delivering agentic AI solutions beyond prototypes, including enterprise deployment considerations.</li>
<li>Strong hands-on engineering background with AWS-based deployments.</li>
<li>Experience working with modern data platforms (e.g., Databricks) and integrating LLM solutions with analytics/data ecosystems.</li>
<li>Ability to operate as a senior individual contributor who can define architecture and implement key pieces end-to-end.</li>
</ul><p>Excellent communication and collaboration skills with US-based stakeholders</p>
<p><b>Skills &amp; Expertise Needed</b></p>
<ul><li><b>Agentic </b><b>AI engineering:</b> building and deploying LLM-powered agents for real business workflows.</li>
<li><b>Agent orchestration:</b> designing multi-step and/or multi-agent flows; managing tool use, control flow, retries, and failure handling.</li>
<li><b>Agent memory:</b> short-term and long-term memory patterns; conversation state; summarization and context window management.</li>
<li><b>Enterprise data access patterns for agents:</b> retrieval/grounding strategies; context assembly from structured and unstructured sources; performance-conscious access.</li>
<li><b>Production deployment mindset:</b> security, reliability, monitoring, and maintainability for enterprise-grade AI services.</li>
<li><b>Architecture &amp; best practices:</b> ability to design scalable components that fit into an existing framework and can be extended by the team.</li>
<li><b>Rapid requirements intake:</b> quickly translating ambiguous reporting/analytics asks into implementable solutions and iterating with stakeholders.</li>
<li><b>High autonomy:</b> tech-lead level capability without direct people management; self-directed and able to set engineering direction for the workstream</li>
</ul><p></p><p><b>Good-to-Have Skills</b></p>
<ul><li>Experience implementing retrieval-augmented generation (RAG) and hybrid retrieval across structured/unstructured sources.</li>
<li>Experience with LLMOps practices: prompt/version management, automated evaluation, and regression testing.</li>
<li>Experience building observability for AI systems (quality metrics, traces, latency/cost monitoring).</li>
<li>Familiarity with Databricks-specific agent or model serving patterns (where applicable).</li>
</ul><p>Experience building lightweight analytics experiences on top of agent outputs (e.g., auto-generated insights/dashboards).</p></div>

AI Tech Lead / AI 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

H2O.ai

<div><p>Founded in 2012, H2O.ai is on a mission to democratize AI. As the world&rsquo;s leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose-built GenAI applications on their private data. With a focus on Sovereign AI&mdash;secure, compliant, and infrastructure-flexible deployments&mdash;H2O.ai delivers solutions that align with the highest standards of data privacy and control.<br>
<br>
Our open-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500. H2O.ai powers AI transformation for companies like AT&amp;T, Commonwealth Bank of Australia, Chipotle, Workday, Progressive Insurance, and NIH.<br>
<br>
H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst &amp; Young (EY), Snowflake, AWS, Google Cloud Platform (GCP), VAST Data and MinIO. H2O.ai&rsquo;s AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation. With a vibrant community of 2 million data scientists worldwide, H2O.ai aims to co-create valuable AI applications for all users.<br>
<br>
H2O.ai has raised 256 million from investors, including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life.<br>
<br>
For more information, visit www.h2o.ai.</p><p><b>
About This Opportunity</b></p><p>
We are looking for a Senior AI Engineer who builds things that matter. You will design and ship end-to-end AI solutions for some of APAC's most complex enterprise problems - spanning agentic AI systems, LLM applications, and production ML pipelines. This is a hands-on engineering role embedded within a customer-facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one.</p><p>
You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos - into production, into workflows, and into measurable business outcomes.<br>
<br>
This position is based in Charlotte, NC</p><p><b>
What You Will Do</b></p><p><i>
Agentic AI &amp; LLM Engineering</i></p><ul><li>
Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers.</li><li>
Develop and deploy LLM-powered applications using techniques including RAG, fine-tuning, prompt engineering, function calling, and tool use.</li><li>
Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety.</li><li>
Stay current with the rapidly evolving agentic AI landscape - MCP, LLM orchestration frameworks, reasoning models - and bring the best of it into customer engagements.</li></ul><p><i>
End-to-End AI Application Development</i></p><ul><li>
Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment.</li><li>
Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows.</li><li>
Integrate AI models into customer environments - cloud, on-prem, and hybrid - ensuring performance, stability, and maintainability at scale.</li><li>
Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production.</li></ul><p><i>
Customer Engagement &amp; Delivery</i></p><ul><li>
Work directly with customer data scientists, engineers, and business stakeholders to translate real-world problems into AI solutions.</li><li>
Contribute to pre-sales and proof-of-concept engagements - building fast, credible demonstrations that win technical trust.</li><li>
Communicate clearly across audiences: from detailed technical design reviews with engineering teams to outcome-focused updates for business stakeholders.</li><li>
Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high-quality solutions.</li></ul><p><b>
What We Are Looking For</b></p><p><i>
Experience &amp; Background</i></p><ul><li>
3+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.</li><li>
Demonstrable experience building LLM-powered applications - RAG pipelines, agentic workflows, fine-tuned models, or similar.</li><li>
Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent).</li><li>
Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes).</li></ul><p><i>
Skills &amp; Capabilities</i></p><ul><li>
Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.</li><li>
Solid grounding in classical ML - able to select the right tool for the problem, not just default to the latest LLM.</li><li>
Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications.</li><li>
Strong problem-solving instincts - comfortable with ambiguity, able to move fast without sacrificing engineering quality.</li><li>
Clear communicator who can explain complex AI systems to non-technical stakeholders without oversimplifying.</li></ul><p><b>
How to Stand Out From the Crowd</b></p><ul><li>
Kaggle or competitive ML experience.</li><li>
Familiarity with H2O.ai products, Wave, or H2O Document AI.</li><li>
Experience in financial services, healthcare, or other regulated industry AI deployments.</li><li>
Exposure to tabular foundation models, AutoML, or enterprise ML platforms.</li><li>
Prior experience in a customer-facing or field engineering role.</li></ul><p><b>
Why H2O.ai?</b></p><ul><li>
Market leader in total rewards</li><li>
Remote-friendly culture</li><li>
Flexible working environment</li><li>
Be part of a world-class team</li><li>
Career growth</li></ul><p>
H2O.ai is committed to creating a diverse and inclusive culture. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis.</p><p>
H2O.ai is an innovative AI cloud platform company, leading the mission to democratize AI for everyone. Thousands of organizations from all over the world have used our cutting-edge technology across a variety of industries. We&rsquo;ve made it easy for people at all levels to generate breakthrough solutions to complex business problems and advance the discovery of new ideas and revenue streams. We push the boundaries of what is possible with artificial intelligence.</p><p>
H2O.ai employs the world&rsquo;s top Kaggle Grandmasters, the community of best-in-the-world machine learning practitioners and data scientists. A strong AI for Good ethos and responsible AI drive the company&rsquo;s purpose.</p><p>
Please visit www.H2O.ai to learn more.</p>
#LI-Hybrid<br>
<p>
</p><p>z4Ve6c4obX</p></div>

Senior AI Engineer

Fusemachines

<div>
 <strong>
  About Fusemachines
  <br/>
  <br/>
 </strong>
 Fusemachines is a leading AI strategy, talent, and education services provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic) and more than 450 full-time employees, Fusemachines brings global AI expertise to transform companies worldwide. Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 <br/>
 <br/>
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 <br/>
 <br/>
 <strong>
  Type: Remote, Full-time
  <br/>
  <br/>
 </strong>
 Role Overview
 <br/>
 <br/>
 As an Applied AI Engineer(Automation), you will deliver high-impact AI and automation solutions for clients&mdash;owning work from requirements discovery through prototype and production deployment. You&rsquo;ll build reliable, maintainable systems that integrate LLMs into real business workflows via APIs, automation platforms, and backend services.
 <br/>
 <br/>
 This is a mid-to-senior individual contributor role. You&rsquo;ll collaborate closely with Solutions Architects, Delivery/Engagement leads, and Product Managers to scope, build, ship, and iterate on client solutions.
 <br/>
 <br/>
 Key Responsibilities
 <br/>
 <br/>
 <ul>
  <li>
   Design &amp; Deploy: Design, develop, and deploy tailored AI and automation solutions aligned to client objectives
  </li>
  <li>
   Build Workflows &amp; Services: Translate business problems into production-grade AI workflows and services using Python, automation tools (n8n/Make/Zapier or similar), and LLM platforms/APIs (e.g., OpenAI, IBM watsonx.ai, Amazon Bedrock), plus retrieval systems
  </li>
  <li>
   Agentic Systems: Build and deploy agentic workflows using LangChain, LangGraph, and Google ADK, including tool calling and structured outputs
  </li>
  <li>
   Retrieval &amp; Knowledge Systems: Implement RAG pipelines using vector databases and search technologies (e.g., Pinecone, Elasticsearch, pgvector) and graph databases when appropriate
  </li>
  <li>
   Prototype &rarr; Production: Ship fast prototypes, then harden them into scalable systems (testing, reliability, deployment, monitoring) independently or with a team
  </li>
  <li>
   Client Partnership: Participate in discovery, run technical calls/demos when needed, and communicate tradeoffs clearly to client and internal stakeholders
  </li>
  <li>
   Ongoing Support &amp; Iteration: Improve deployed solutions through feature work, bug fixes, monitoring, prompt/model improvements, and additional automations
  </li>
  <li>
   Documentation: Produce clear technical documentation, client demos, and internal playbooks to enable reuse and scalability
  </li>
  <li>
   Continuous Learning: Stay current on LLM tooling and delivery best practices to improve quality and speed
   <br/>
   <br/>
  </li>
 </ul>
 Success in This Role Looks Like
 <br/>
 <br/>
 <ul>
  <li>
   Solutions consistently meet or exceed client expectations and show measurable impact (time saved, cost reduced, improved conversion/deflection, faster cycle time)
  </li>
  <li>
   Clients trust you as a go-to engineering partner and expand usage of deployed AI workflows
  </li>
  <li>
   Deliveries are production-ready: monitored, testable, documented, and maintainable
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   3&ndash;8 years of software or AI engineering experience (mid-to-senior)
  </li>
  <li>
   2&ndash;3+ years of AI Automation, Generative AI, or Agentic AI (mid-to-senior)
  </li>
  <li>
   Strong Python engineering skills and experience building APIs/services (e.g., FastAPI)
  </li>
  <li>
   Hands-on experience integrating LLMs (e.g., OpenAI APIs or equivalents), including prompt design, structured outputs, and basic evaluation practices
  </li>
  <li>
   Experience with at least one workflow automation platform (n8n, Make, Zapier, or similar) and building reliable integrations
  </li>
  <li>
   Familiarity with RAG fundamentals and retrieval systems (embeddings, vector search); exposure to vector databases and/or Elasticsearch
  </li>
  <li>
   Production engineering fundamentals: Docker, cloud deployment (AWS/GCP/Azure/IBM), and experience with async/queuing patterns (e.g., Celery, Redis, Kafka)
  </li>
  <li>
   Comfort operating in a client-facing environment: technical calls, demos, and collaborating with cross-functional stakeholders
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with fine-tuning LLMs or other ML models; broader ML exposure is a plus (not required)
  </li>
  <li>
   Familiarity with observability and tracing (e.g., LangSmith, OpenTelemetry) and prompt/version lifecycle management
  </li>
  <li>
   Experience with graph databases / knowledge graphs
  </li>
  <li>
   Familiarity with data governance and AI governance concepts (PII handling, auditability, access controls, risk awareness)
  </li>
  <li>
   Prior consulting experience or work in fast-paced startup environments
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
  <br/>
  <br/>
 </em>
 Powered by JazzHR
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 <br/>
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</div>


Applied AI Engineer (Automation)

Search 9734+ Agentic ai engineer jobs near you this April. New roles added regularly across leading industries. Apply today.

9734 agentic ai engineer jobs Near Me

Agentic AI Engineer

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Applied AI Engineer (Automation)