engineering agent

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> 
</div>
<div></div> 
 
<div>Salary Range: $130,000 to $170,000 per year
</div></div></div></div><div><div><div>Location</div>
<div>New York, NY</div>
</div><div><div>Job Function</div>
<div>TECHNOLOGY</div>
</div><div><div>Role</div>
<div>Engineer</div>
</div><div><div>Job Id</div>
<div>406637</div>
</div><div><div>Desired Skills</div>
<div>Artificial Intelligence</div>
</div><div><div>Salary Range</div>
<div>$130,000-$170,000 a year</div>
</div></div>Desired Candidate Profile
<div><div>Qualifications : BACHELOR OF COMPUTER SCIENCE</div></div></div><div></div>

GEN AI and AGENTIC AI Engineer

Provident Real Estate

<div>
 
 
 
 About Provident Real Estate
 
 Established in 2008, Provident Real Estate has grown into one of Dubai's leading real estate agencies, built on transparency, honesty, and professionalism. With a full-service approach, we support clients across buying, leasing, property management, financing solutions, and holiday homes. But what truly sets us apart is how seriously we take execution.
 
 
 Our marketing team of 42 specialists is one of the largest and most driven real estate marketing teams in Dubai. We don't just market properties &mdash; we build systems, standards, and trust at scale. Representing 40+ nationalities and 60+ languages, Provident Real Estate is a place where ownership is encouraged, ideas are respected, and performance is visible. If you bring passion and discipline, there are no limits to how far you can grow here.
 
 
 
 
 
 
 About the role:
 
 We're looking for an AI Engineer who lives and breathes intelligent systems. You'll design and ship production-grade AI agents, architect multi-step AI workflows, and help define how our products think, reason, and act. This is a hands-on role &mdash; you'll be building the systems that matter, not just prototyping them.
 
 
 
 
 
 
 What you'll do
 
 
 <ul>
 <li>
 Design and build AI agents &mdash; autonomous, tool-using, goal-directed agents for real-world tasks using frameworks like LangChain, LangGraph, AutoGen, or custom architectures.
 </li>
 <li>
 Define AI workflows &mdash; architect reliable, observable pipelines that chain LLM calls, tool use, memory, and structured outputs together end-to-end.
 </li>
 <li>
 Own the full stack &mdash; from prompt design and model selection to deployment, monitoring, and iteration based on production feedback.
 </li>
 <li>
 Work with RAG and memory systems &mdash; integrate vector databases, knowledge retrieval, and context management into agent pipelines.
 </li>
 <li>
 Evaluate and improve &mdash; build evals, measure agent performance, and drive quality improvements through systematic experimentation.
 </li>
 <li>
 Collaborate cross-functionally &mdash; partner with product, data, and platform engineers to translate requirements into reliable AI-powered systems.
 </li>
 </ul>
 <li>
 
 </li>
 
 
 Must-have skills
 
 
 <ul>
 <li>
 3+ years of software engineering with strong Python proficiency.
 </li>
 <li>
 Hands-on experience building LLM-powered applications or agents.
 </li>
 <li>
 Familiarity with agent frameworks &mdash; LangChain, LangGraph, AutoGen, CrewAI, or similar.
 </li>
 <li>
 Solid grasp of prompt engineering, function calling, and tool use.
 </li>
 <li>
 Experience with APIs (REST, async) and cloud services &mdash; AWS, GCP, or Azure.
 </li>
 <li>
 Ability to reason about latency, cost, and reliability tradeoffs in AI systems.
 </li>
 </ul>
 <li>
 
 </li>
 
 
 Nice to have
 
 
 <ul>
 <li>
 Experience with vector databases &mdash; Pinecone, Weaviate, pgvector, or Chroma.
 </li>
 <li>
 Knowledge of fine-tuning or RLHF pipelines.
 </li>
 <li>
 Familiarity with MLOps tools &mdash; MLflow, Weights &amp; Biases, etc.
 </li>
 <li>
 Prior work on multi-agent systems or agentic reasoning frameworks.
 </li>
 <li>
 Contributions to open-source AI projects.
 </li>
 </ul>
 
 
 
 
 If you feel you're the right fit, Send your CV along with a brief note on why you're the right fit to
 
 marketing.ads@providentestate.com
 
 
 
 
 
 
 
 PLEASE NOTE:
 
 
 Only CV sent via EMAIL will be screened. NOT EASY APPLY.
 
 
</div>

AI Engineer - Agents & Workflows

FocusKPI Inc.

<div>We are seeking an Agentic AI Engineer to support the development of a customized AI agent solution for one of our clients. 
 
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. 
 
Environment: 
 
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. 
 
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. 
 
Work Location: Remote position (40 hours per week) 
Duration: Full-time role 
Pay-rate: $30+/hr 
 
**No C2C resumes are considered** 
 
Responsibilities:<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>
Qualifications:<ul><li>
A minimum of 2 years of experience is required</li><li>
Education requirements: Master's degree</li><li>
Mandatory skills: 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>
Nice to have:<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>
Thank you!
FocusKPI Hiring Team
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.
NOTICE: 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/ . 
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.
DC4HSZaJiV</div>

Agentic AI Engineer

Etera Technologies FZCO

<div>About Etera
Etera is building the first AI-native corporate travel platform for the GCC market &mdash; designed from the ground up for how businesses in the region actually operate. We're a lean, high-conviction team replacing legacy TMCs with autonomous agents, real-time inventory, and a product experience that doesn't feel like it was designed in 2009.
The Role
We're looking for a Senior AI Fullstack Engineer who can own the full stack &mdash; from a smooth React Native mobile experience to TypeScript backend services and the AI/LLM layer that powers our multi-agent infrastructure (OPUS Prime). If you think in systems, ship fast, and find it natural to reason about both user-facing product and underlying model behavior, this role was built for you.
What You'll Own<ul><li>
Mobile-first product: Build and maintain Etera's React Native app (Expo/TypeScript) &mdash; booking flows, real-time updates, multilingual UX (Arabic/English).</li><li>
Backend services: Design and extend our TypeScript backend, including booking APIs, supplier integrations (PKfare, RateHawk, Hotelbeds), and webhook orchestration.</li><li>
LLM integration: Work directly with Claude (Anthropic API) and our multi-agent OPUS Prime system &mdash; building, tuning, and extending AI pipelines for travel intent detection, QA, and automated engineering reports.</li><li>
End-to-end delivery: Take features from spec to production. Own the full lifecycle &mdash; architecture, implementation, testing, deployment.</li><li>
Infra &amp; reliability: Firebase Auth (OTP, Apple, Google), EAS Build/TestFlight pipelines, and CI/CD hygiene across the stack.</li><li>
Code quality: Participate in security audits and peer review within a small, high-trust engineering team.</li></ul> 
Tech Stack
Frontend / Mobile: React Native + Expo, TypeScript, Firebase Auth (OTP, Apple, Google), EAS Build / TestFlight, Native iOS/Android context.
Backend / AI: TypeScript (end-to-end), Anthropic API / Claude, Mastra (agent orchestration framework), LangChain (chains, tools, memory), Zeabur / cloud deployment, REST + Webhook integrations.
You've Built and Run Agent Teams. Plural.
This is not a role for someone who has experimented with LLMs on side projects. We're looking for engineers who have designed, shipped, and actively maintained multi-agent systems &mdash; and who understand what it takes to keep them reliable in production.<ul><li>
Proven agent team ownership: You have built one or more autonomous agent teams from scratch &mdash; defined their scope, wired their tools, managed inter-agent communication, and own their continued operation.</li><li>
Self-validating mindset: You don't hand off agent output blindly. You act as the primary validation layer for the systems you build &mdash; designing evaluation criteria, catching failure modes, and iterating on agent behavior based on real output quality.</li><li>
Cross-domain fluency: Your agents have touched multiple domains &mdash; not just one narrow use case. You can reason about agent behavior across booking logic, data quality, QA pipelines, code review, or content generation without needing a specialist in each area.</li><li>
Maintenance discipline: You have a track record of keeping agent systems healthy over time &mdash; prompt drift, tool failures, context window management, regression testing. Not just shipping and moving on.</li><li>
Full-stack integration: You connect agent teams to real product surfaces &mdash; not just notebooks or internal tools. Your work shows up in APIs, mobile interfaces, or backend workflows that real users depend on.</li></ul> 

Requirements 
<ul><li>
5+ years of professional fullstack engineering, with meaningful production work in mobile.</li><li>
Strong React Native track record &mdash; shipped apps, managed build pipelines, handled auth flows.</li><li>
TypeScript across the entire stack &mdash; mobile, backend, and agent tooling. This is non-negotiable.</li><li>
Hands-on with LLM APIs (Anthropic, OpenAI, or equivalent) &mdash; prompt engineering, tool/function calling, multi-turn context management.</li><li>
AI-augmented development: You use Claude Code, Codex, and similar agentic coding tools to achieve 10x engineering throughput. You know how to create, configure, and orchestrate coding agents &mdash; not just prompt them &mdash; and you treat them as force multipliers across your daily workflow.</li><li>
Mastra: Production experience with Mastra as an agent orchestration framework &mdash; workflows, tool definitions, agent memory, and inter-agent coordination patterns.</li><li>
LangChain: Solid working knowledge &mdash; chains, retrieval, tool use, memory backends, and integration into live backend services. Not just tutorials.</li><li>
Ability to operate as a full engineering team member &mdash; not a specialist silo. You ship frontend, backend, and AI layers as needed, and you hold the full picture in your head simultaneously.</li><li>
Comfort in a lean startup environment: fast cycles, high ownership, minimal process overhead.</li><li>
Strong written English. Arabic or German is a genuine plus given our markets.</li></ul>
Bonus Points<ul><li>
Prior work with GDS/NDC/travel supplier APIs (Amadeus, Sabre, PKfare, RateHawk).</li><li>
Experience building multi-agent AI systems or autonomous workflow orchestration.</li><li>
Experience with Voice AI agents (Ultravox, Vapi, ElevenLabs, or equivalent) &mdash; building or integrating automated voice calling systems.</li><li>
Familiarity with DIFC / UAE regulatory context for SaaS or fintech products.</li><li>
Background in security auditing or static code analysis tooling.</li></ul> 

Benefits 
<ul><li>
Greenfield Ownership &mdash; You won't inherit a decade of technical debt. You'll shape the architecture from the ground up.</li><li>
Real AI Work &mdash; Not AI as a buzzword. Actual multi-agent systems in production, with full access to our OPUS Prime infrastructure.</li><li>
Remote-First &mdash; Work from anywhere. Async-friendly culture with a team distributed across Europe and the Gulf.</li><li>
Equity + Competitive Package &mdash; Meaningful equity stake. Competitive cash compensation. Built for the long term.</li><li>
High-Impact Market &mdash; GCC corporate travel is a $20B+ market with legacy infrastructure. You'll be building the replacement.</li><li>
Fast Cycles &mdash; No committee approvals. Ship, learn, iterate. If it breaks, fix it and ship again &mdash; better.</li></ul></div>

Senior AI Fullstack Engineer

AR5 Labs

AI Engineering Intern &mdash; AR5 LabsRemote | InternshipABOUT AR5 LABSAR5 Labs builds AI-powered automation tools for data analytics. Our flagship product, PlotStudio AI, is a desktop analytics platform where specialized AI agents collaborate through a multi-stage pipeline to deliver the kind of analysis a senior data scientist would produce, complete with code, visualizations, statistical rigor, and executive-grade narrative. Think Cursor, but for data analytics.Product: https://www.plotstudio.ai/LinkedIn: https://www.linkedin.com/company/ar5-labs/ABOUT THE ROLEWe're looking for an AI Engineering Intern to help build and improve the AI agent pipeline behind PlotStudio AI.This is not a research internship. You'll work on production AI systems: prompt engineering, agent orchestration, model evaluation, and the infrastructure that makes multi-agent workflows reliable. You'll see how AI agents are actually built, tested, and shipped to real users.You'll work directly with the founder and the AI/ML team. The codebase is small enough that your contributions will ship to production quickly, and complex enough that you'll learn how a real multi-agent system works from the inside.If you're a student who wants to build AI systems, not just study them, this is the role.RESPONSIBILITIES- Help design, test, and iterate on prompts for PlotStudio's multi-agent pipeline (routing, planning, coding, interpretation agents)- Build evaluation scripts to benchmark agent output quality across different datasets and question types- Run experiments comparing model performance (GPT-4.1, GPT-5.1, Claude, Gemini) on specific analytical tasks- Help improve agent self-correction logic: how the system detects errors, retries, and adjusts its approach- Work on domain skill modules that inject specialized knowledge (finance, healthcare, econometrics) into the agent pipeline- Build internal tooling for tracking agent accuracy, token usage, and failure modes- Help extend the agent orchestration layer (built with LangGraph) to support new workflow types- Document prompt versions, experiment results, and engineering decisions as you goQUALIFICATIONSRequired:- Currently pursuing a degree in Computer Science, Data Science, AI/ML, or a related field (rising sophomore, junior, or senior)- Comfortable writing Python. You should be able to read, write, and debug code independently- Basic understanding of how LLMs work: prompts, tokens, context windows, temperature- Genuine interest in AI agents, not just chatbots. You should have an opinion on why single-prompt AI falls short for complex tasks- Willingness to experiment, break things, and iterate quickly- Self-directed and comfortable asking questions when you're stuck- Able to commit 15-20 hours per weekNice to have:- Experience using the OpenAI API, Anthropic API, or any LLM SDK- Familiarity with LangChain, LangGraph, CrewAI, or any agent framework- Personal projects involving LLMs, prompt engineering, or AI agents- Coursework in machine learning, NLP, or statistics- Familiarity with Git and version control- Experience with FastAPI, Flask, or any Python backend framework- Understanding of statistical methods (regression, hypothesis testing, time series)WHAT YOU'LL LEARN- How a production multi-agent system is built: routing, planning, execution, self-correction, and interpretation agents working in sequence- Prompt engineering at a level most people never see: versioned prompts, domain-aware injection, structured output parsing, and quality gates between agents- How to evaluate AI systems beyond vibes: building benchmarks, measuring accuracy, tracking regressions across prompt versions- How to ship AI to real users in a small, fast-moving teamWHY THIS INTERNSHIP- You'll work on a real multi-agent AI system, not a tutorial project- Small team means high visibility and direct mentorship from the founder- Your work will ship to production and be used by real customers- You'll have something concrete for your resume: &quot;I helped build and evaluate the AI agent pipeline for a production analytics platform&quot;MUST HAVEWe are across the United States and Canada. Must be a citizen of either country.HOW TO APPLYFollow our LinkedIn page and DM us directly, or email us:1. A short intro about yourself2. Your resume or LinkedIn profile3. What year you're in and what you're studying4. Why you want to work at AR5 LabsEmail: plotstudio@ar5labs.comLinkedIn: https://www.linkedin.com/company/ar5-labs/Pay: $20.00 - $45.00 per hourWork Location: Remote

AI Engineer Intern

VeeRteq Solutions Inc.

<div>We are seeking a high-caliber AI Tech Lead / AI Engineer 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.
Responsibilities: -
<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>Tech Stack (Core): -
<ul><li>Cloud: AWS (primary deployment environment; open to alternatives)</li>
<li>Data/Analytics Platform: Databricks (including native &quot;chat with data&quot; capabilities and potential agent integrations)</li>
<li>Agent Frameworks: LangChain, LangGraph</li>
<li>Conversational analytics patterns: Ask-questions-on-data / conversational BI approaches (agent-driven analytics and dashboards</li>
</ul>Experience: -
10+ Years
Location: -
Rosemont, IL (3days/Week)
Educational Qualifications: -
Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
Technical certification in multiple technologies is desirable.
Skills: -
Mandatory skills
<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>Excellent communication and collaboration skills with US-based stakeholders
Skills &amp; Expertise Needed
<ul><li>Agentic AI engineering: building and deploying LLM-powered agents for real business workflows.</li>
<li>Agent orchestration: designing multi-step and/or multi-agent flows; managing tool use, control flow, retries, and failure handling.</li>
<li>Agent memory: short-term and long-term memory patterns; conversation state; summarization and context window management.</li>
<li>Enterprise data access patterns for agents: retrieval/grounding strategies; context assembly from structured and unstructured sources; performance-conscious access.</li>
<li>Production deployment mindset: security, reliability, monitoring, and maintainability for enterprise-grade AI services.</li>
<li>Architecture &amp; best practices: ability to design scalable components that fit into an existing framework and can be extended by the team.</li>
<li>Rapid requirements intake: quickly translating ambiguous reporting/analytics asks into implementable solutions and iterating with stakeholders.</li>
<li>High autonomy: tech-lead level capability without direct people management; self-directed and able to set engineering direction for the workstream</li>
</ul>Good-to-Have Skills
<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>Experience building lightweight analytics experiences on top of agent outputs (e.g., auto-generated insights/dashboards).</div>

AI Tech Lead / AI Engineer

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