engineering agent

MASTER-WORKS

<div>Master Works is looking for an Agentic AI, Engineer to join our growing team and design, build, and deploy agentic AI systems that can plan, reason, and act. The role covers both single-agent and multi-agent systems, as well as orchestration of workflows involving retrieval-augmented generation (RAG), context engineering, reasoning, tool calling, memory, and inter-agent communication. This role is ideal for a strong software engineer with hands-on AI/ML development experience and a passion for transforming generative AI models into reliable, goal-driven systems capable of solving complex, real-world business problems.
Key Responsibilities:<ul><li>
Architect, build, and deploy agentic AI systems that integrate AI agents with foundation models, enterprise systems, third-party tools, and APIs using established agentic frameworks or custom-built orchestrators.</li><li>
Build and maintain retrieval-augmented generation (RAG), memory, and reasoning pipelines that ground agent decisions in reliable, real-world data and enable persistent, contextual, and adaptive behavior.</li><li>
Design single-agent and multi-agent workflows, including planning, delegation, tool selection, handoffs, inter-agent communication, and human-in-the-loop controls.</li><li>
Optimize orchestration and reasoning performance while balancing autonomy, latency, cost, interpretability, reliability, and maintainability.</li><li>
Collaborate with Generative AI engineers, application engineers, MLOps engineers, and product teams to move agentic AI solutions from prototype to production.</li><li>
Monitor, evaluate, and benchmark agent performance, and help ensure AI systems are safe, accurate, trustworthy, observable, and capable of delivering a high-quality user experience.</li><li>
Document agent architectures, communication flows, guardrails, context-engineering strategies, memory design, tool interfaces, and orchestration logic to ensure clarity and reproducibility.</li><li>
Stay current with advances in agentic AI, multi-agent orchestration, RAG, model-context protocols, cognitive architectures, evaluation methods, and AI safety mechanisms.</li><li>
Collaborate with cross-functional teams to design and deliver complete AI solutions for customers.</li></ul> 

Requirements 
<ul><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.</li><li>
2+ years of hands-on experience developing agentic AI systems, LLM-powered applications, or advanced Generative AI solutions.</li><li>
Preferably 5+ years of overall professional software engineering experience</li><li>
Contributions to open-source agentic AI frameworks, orchestration platforms, evaluation tooling, or agent communication protocols.</li><li>
Experience building microservices and deploying AI applications on cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).</li><li>
Experience with AI safety, guardrails, observability, traceability, evaluation, and explainability for agentic AI systems.</li><li>
Experience deploying LLM or agentic workloads in production using containers, Kubernetes, CI/CD pipelines, and modern MLOps or LLMOps practices.</li></ul>
Job Location: HQ</div>

Agentic AI, Engineer

DICETEK LLC

<div>We are looking for an experienced AI Engineer responsible for designing, developing, and deploying Generative AI and Agentic AI solutions that support enterprise-scale business use cases. The role will build intelligent systems that integrate complex backend services and client-facing applications across web, mobile and enterprise platforms.
The primary responsibility is to design and develop AI-powered applications, autonomous agents and multi-agent workflows while coordinating with cross-functional teams across architecture, engineering, product and business functions. A commitment to collaborative problem solving, sophisticated design and product quality is essential.
This role requires strong hands-on engineering experience, practical working knowledge of modern agent frameworks, and the ability to deliver secure, scalable, observable and governed AI solutions in cloud-native environments. 
QUALIFICATIONS &amp; EXPERIENCE:
Minimum Qualification<ul><li>Bachelor&rsquo;s degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence or a related discipline.</li><li>Relevant cloud, AI engineering, machine learning or architecture certifications are preferred.</li></ul> 
Minimum Experience<ul><li>Senior professional with around 10 years of total software engineering, architecture, cloud or platform engineering experience.</li><li>Minimum 3+ years of relevant hands-on AI Engineering experience, including Generative AI and practical LLM-based application delivery.</li><li>Strong proficiency in Python, including NumPy, pandas, FastAPI and hands-on experience with PyTorch or TensorFlow.</li><li>Hands-on experience with LangChain and LangGraph; mandatory working experience with Microsoft Semantic Kernel and Microsoft AutoGen.</li><li>Experience implementing RAG using embeddings, vector databases, semantic search, retrieval optimization and model evaluation techniques.</li><li>Experience deploying and managing models using Amazon Bedrock, Azure OpenAI Service and Google Vertex AI.</li><li>Hands-on experience with microservices, containers, APIs, event-driven architecture, cloud-native services and evolutionary architecture practices.</li><li>Experience managing and deploying AI workloads on Kubernetes in cloud-native and/or hybrid environments.</li><li>Experience with CI/CD tools such as Jenkins or GitLab, DevOps toolchains, configuration management and cloud/on-prem deployment pipelines.</li><li>Experience setting up pipelines with static code analysis, requirement tagging in Jira, quality gates and release governance.</li><li>Experience operating monitoring tools for traditional infrastructure, cloud environments and AI-enabled business applications.</li><li>Strong hands-on problem-solving mindset with the ability to analyze trade-offs and deliver sustainable, secure and high-quality solutions.</li></ul> 
Key Technical Skills<ul><li>Generative AI, Agentic AI, autonomous agents, multi-agent orchestration and workflow-based AI systems.</li><li>LLMs, embeddings, vector databases, RAG, semantic search, model evaluation, guardrails, observability and AI governance.</li><li>Semantic Kernel, AutoGen, LangChain, LangGraph and similar agent frameworks.</li><li>Python, FastAPI, PyTorch/TensorFlow, REST APIs, microservices, serverless functions and event-driven integration.</li><li>Azure, AWS, Kubernetes, containers, CI/CD, DevOps automation, monitoring and secure software delivery.</li></ul>
Behavioural / Leadership Skills<ul><li>Strong collaborative mindset for agile architecture and decentralized decision making.</li><li>Proactive, positive and growth-oriented leadership style with the ability to motivate engineers and foster craftsmanship.</li><li>Strong communication, stakeholder engagement and influencing skills across product, business, architecture and engineering teams.</li><li>Analytical, system-thinking and pragmatic problem-solving approach with commitment to product quality.</li></ul> 
Skills: Generative AI , Python , Kubernetes</div>

AI Engineer – Generative AI & Agentic AI

Deloitte

<div>
 Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.
 
 
 This is an early, well-funded build. You will own agent systems end to end - from architecture through production - and your work ships into live clinical and operational settings within your first months, not into a lab.
 
 
 As an Agentic AI Engineer, you will design, build, and operationalize the LLM- and SLM-powered systems behind real healthcare decisioning - the reasoning, orchestration, retrieval, memory, and control layers that let intelligent agents operate reliably across the hardest decisions in the industry: clinical reasoning, prior authorization and claims integrity, care navigation, and the operational workflows that run across payers, providers, and life sciences. This is not a prompt-only role. We are looking for builders who think deeply about system behavior, grounding, and reliability where a wrong action has real consequences for patients and the clinicians who serve them.
 
 
 You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the agentic engineering depth.
 
 
 We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.
 
 
 Work you'll do
 
 
 Agent architecture &amp; orchestration
 
 
 <ul>
 <li>
 Design and implement agentic systems capable of multi-step reasoning, planning, tool use, and workflow execution against complex, regulated operational processes.
 </li>
 <li>
 Build stateful workflows using frameworks such as LangGraph and LangChain - including branching, retries, self-correction, human-in-the-loop checkpoints, and reusable orchestration patterns.
 </li>
 <li>
 Engineer for long-horizon reliability - multi-step task completion, recovery from compounding errors, planning under uncertainty, and robust tool use when individual steps fail.
 </li>
 <li>
 Build the reasoning behind regulated decisions - policy- and criteria-grounded outputs, structured proposer/critic/judge-style review, and auditable rationales for high-stakes decisions across the industry, from clinical review and prior authorization to claims integrity and care management.
 
 
 </li>
 </ul>
 Retrieval, grounding &amp; context engineering
 
 
 <ul>
 <li>
 Develop end-to-end Retrieval-Augmented Generation (RAG) pipelines: ingestion, chunking, embeddings, vector and hybrid retrieval, reranking, contextual compression, and grounding strategies.
 </li>
 <li>
 Engineer memory and context management - conversational state, persistent memory, retrieval-aware context assembly, and token-efficient context selection.
 </li>
 <li>
 Apply modern context-delivery patterns (e.g., MCP-style tool/context interfaces) so agents access the right information at the right time.
 
 
 </li>
 </ul>
 Reliability, evaluation &amp; safety
 
 
 <ul>
 <li>
 Implement observability and tracing for prompts, tool calls, retrieval quality, agent traces, failures, drift, latency, and production behavior.
 </li>
 <li>
 Apply guardrails, safety controls, and failure-handling to reduce hallucinations and unsafe actions.
 </li>
 <li>
 Evaluate agents at the trajectory and task level - multi-step task success, failure-mode and regression analysis, and sandboxed test environments - alongside retrieval- and generation-quality metrics, automated checks, and human review.
 </li>
 <li>
 Engineer healthcare-grade safety - deployment eval gates, human-oversight and escalation models, auditability and traceability for regulated decisions, and PHI/HIPAA-aware data handling.
 
 
 </li>
 </ul>
 Integration &amp; production craft
 
 
 <ul>
 <li>
 Build integrations with internal and external tools, APIs, enterprise systems, databases, and model providers so agents operate safely within real business workflows.
 </li>
 <li>
 Deliver production-quality code with strong practices in testing, CI/CD, logging, versioning, and documentation; make architecture decisions that balance quality, safety, latency, cost, and model risk.
 </li>
 <li>
 Partner with our modeling and post-training engineers to improve model behavior for tool use, grounding, and long-horizon reasoning - through evaluation-driven feedback and, where it helps, fine-tuned or reasoning-optimized models.
 </li>
 <li>
 Translate ambiguous, high-complexity operational processes into robust system logic and reusable AI patterns; stay current with advances in agentic systems and translate research into practical engineering decisions.
 
 
 </li>
 </ul>
 The team
 
 
 Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
 
 
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Engineering, Data Science, Computational Linguistics, or a related field.
 </li>
 <li>
 Demonstrated depth building and shipping production agentic systems - this is your primary craft, not a recent exploration. We weigh shipped systems, research, model releases, and open source over years in a title; expect strong software/ML fundamentals plus substantial, recent hands-on agentic work.
 </li>
 <li>
 Strong, hands-on experience building production agent systems with modern orchestration - LangGraph/LangChain or equivalent, including custom orchestration.
 </li>
 <li>
 Experience designing and optimizing end-to-end RAG systems: indexing, retrieval, reranking, grounding, and evaluation.
 </li>
 <li>
 Strong understanding of memory and context management, including context windows, retrieval-driven context assembly, persistent memory, and high-signal context selection.
 </li>
 <li>
 Deep, practical understanding of LLM behavior - strengths, limitations, hallucination risks, reasoning constraints, and latency/cost trade-offs - and the evaluation methods used to measure them.
 </li>
 <li>
 Experience evaluating and debugging agent behavior - task-success and trajectory analysis, not just output quality.
 </li>
 <li>
 Strong Python engineering skills and modern software practices: testing, CI/CD, version control, and API integration; experience implementing observability, tracing, and debugging for LLM-based systems in production.
 </li>
 <li>
 Hands-on experience with at least one frontier model platform (e.g., Anthropic, Google, OpenAI) and/or open-weight/self-hosted models (e.g., Llama via vLLM), including production tool use and agent capabilities.
 </li>
 <li>
 Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
 </li>
 <li>
 Limited immigration sponsorship may be available.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Experience with multi-agent systems and agent collaboration patterns.
 </li>
 <li>
 Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus.
 </li>
 <li>
 Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA.
 </li>
 <li>
 Understanding of traditional NLP concepts: tokenization, semantic similarity, entity extraction, summarization, and transformer fundamentals.
 </li>
 <li>
 Experience operating in highly regulated, high-stakes, or operationally complex environments; healthcare exposure - clinical, payer, or life-sciences workflows, or standards such as FHIR - is a plus, not a requirement.
 </li>
 <li>
 Demonstrated habit of staying current with AI research, benchmarks, and emerging engineering patterns.
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $110,700-$372,900 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.
</div>

Agentic AI Engineer — Healthcare AI

<div>
 
 About Us
 
 
 
 We are the independent expert in assurance and risk management. Driven by our purpose, to safeguard life, property, and the environment, we empower our customers and their stakeholders with facts and reliable insights so that critical decisions can be made with confidence.
 
 
 As a trusted voice for many of the world&rsquo;s most successful organizations, we use our knowledge to advance safety and performance, set industry benchmarks, and inspire and invent solutions to tackle global transformations.
 
 
 
 About Energy Systems
 
 
 
 We help customers navigate the complex transition to a decarbonized and more sustainable energy future. We do this by assuring that energy systems work safely and effectively, using solutions that are increasingly digital. We also help industries and governments to navigate the many complex, interrelated transitions taking place globally and regionally, in the energy industry.
 
 
 
 About The Role
 
 
 
 At Digital and Data Solutions (DDS), we develop innovative software and data-driven solutions that help customers solve complex challenges in energy, infrastructure, and sustainability. Together, we combine technology and trusted expertise to create lasting impact and accelerate the transition to a more sustainable world.
 
 
 We are looking for an AI Engineer specializing in agentic systems to help design, build, productise, and evolve generative AI capabilities within Horizon, cloud platform for renewable energy asset monitoring, analytics, optimisation, and decision support.
 
 
 This is a hands-on, product-focused engineering role. You will build production-grade AI agents that interact with Horizon data, analytics, algorithms, APIs, documentation, and operational workflows. The focus will be on building a role-based multi-agent system supporting users such as Asset Managers, Asset Owners, O&amp;M Managers, Traders, and Analysts.
 
 
 
 What You&rsquo;ll Do
 
 
 
 <ul>
 <li>
 Design and implement a scalable architecture for role-based AI agents, including agents supporting Asset Management, Asset Ownership, O&amp;M, predictive maintenance, trading, and analytical workflows.
 </li>
 <li>
 Define agents responsibilities, tools, interaction patterns, task delegation, context sharing, and hand-offs between specialised agents.
 </li>
 <li>
 Integrate agents with diverse Horizon data sources, including operational time-series data, alarms, events, asset metadata, analytical results, forecasts, reports, logbooks, and technical documentation.
 </li>
 <li>
 Build evaluation frameworks and representative test datasets covering answer quality, groundedness, tool selection, workflow completion, hallucination risk, safety, regression, latency, and operational reliability.
 </li>
 <li>
 Implement observability and traceability across agent execution, including prompts, retrieved context, tool calls, model responses, decisions, failures, and user feedback.
 </li>
 <li>
 Deploy and operate AI services in production, working with software and platform engineers.
 </li>
 <li>
 Collaborate with renewable energy domain experts to ensure that agent outputs are technically meaningful, evidence-based, and appropriate for operational decision-making.
 </li>
 <li>
 Stay informed about developments in LLMs, agent orchestration, multimodal systems, evaluation, and AI engineering, and assess them pragmatically for production use.
 
 
 </li>
 </ul>
 What makes this role exciting: you&rsquo;ll take cutting-edge agent-based systems out of the lab and into production, building AI that directly supports the operation and optimization of renewable energy assets at global scale.
 
 
 What we offer
 
 
 
 Our benefits package is specifically designed to support your physical, financial and social well-being:
 
 
 
 <ul>
 <li>
 Great atmosphere of working together with professionals and some of the most engaged and knowledgeable people in the industry,
 </li>
 <li>
 Receive guidance from colleagues through coaching, mentoring and participating in international networks,
 </li>
 <li>
 Advance your professional skills and technical expertise, through individual competence development plans and tailored training,
 </li>
 <li>
 Be part of a world growing and renowned organization with origins dating back to 1864.
 
 
 </li>
 </ul>
 
 Other Than You Can Expect
 
 
 
 <ul>
 <li>
 Medical Scheme,
 </li>
 <li>
 Commuting Allowance,
 </li>
 <li>
 Life Insurance,
 </li>
 <li>
 Pension Plan,
 </li>
 <li>
 Kindergarten Allowance,
 </li>
 <li>
 40 hours per week with a flexible schedule. Friday
 </li>
 <li>
 Home working allowance (up to 2 days per week),
 </li>
 <li>
 23 days of annual leave,
 </li>
 <li>
 Employee Referral scheme.
 
 
 </li>
 </ul>
 DNV is an Equal Opportunity Employer and gives consideration for employment to qualified applicants without regard to gender, religion, race, national or ethnic origin, cultural background, social group, disability, sexual orientation, gender identity, marital status, age or political opinion. Diversity is fundamental to our culture and we invite you to be part of this diversity.
 
 
 
 Requirements
 
 
 
 About you
 
 
 <ul>
 <li>
 Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field, or equivalent practical experience.
 </li>
 <li>
 Proven experience building AI agents, agentic workflows, or AI assistants, preferably in production environments.
 </li>
 <li>
 Experience with Retrieval-Augmented Generation (RAG), embeddings, vector search, and prompt engineering.
 </li>
 <li>
 Strong Python software engineering skills and experience developing production-grade applications.
 </li>
 <li>
 Experience integrating AI solutions with APIs, databases, and enterprise systems.
 </li>
 <li>
 Familiarity with Git, CI/CD, Docker, and cloud-native development practices.
 </li>
 <li>
 Strong communication skills and fluency in written and spoken English.
 
 
 </li>
 </ul>
 
 Nice To Have
 
 
 
 <ul>
 <li>
 Experience deploying AI workloads on Kubernetes.
 </li>
 <li>
 Experience with self-hosted LLMs and model serving.
 </li>
 <li>
 Familiarity with ClickHouse, MongoDB, vector databases and time-series data platforms.
 
 
 </li>
 </ul>
 You are curious, adaptable, and proactive, thriving in collaborative, fast-moving environments. You take ownership of projects, communicate complex AI concepts clearly to technical and non-technical stakeholders, and enjoy solving challenging real-world problems.
 
 
 As part of the interview process, we ask you to submit a short report or demo of an AI agent you&rsquo;ve built. This is your chance to showcase your hands-on skills and highlight your creativity.
 
 
 
 Security and compliance with statutory requirements in the countries in which we operate is essential for DNV. Background checks will be conducted on all final candidates as part of the offer process, in accordance with applicable country-specific laws and practices.
 
</div>

AI Engineer - Agentic Systems

Müller's Solutions

<div>
 
 Job Description
 
 
 
 We are seeking experienced
 
 AI Engineers / Agentic AI Developers
 
 to design, develop, and deploy enterprise-grade AI solutions with a focus on
 
 Agentic AI
 
 ,
 
 Large Language Models (LLMs)
 
 , and
 
 Retrieval-Augmented Generation (RAG)
 
 . The successful candidate will be responsible for building intelligent AI agents and workflows capable of reasoning, planning, task execution, and orchestration while ensuring all solutions are deployed in an
 
 on-premises environment
 
 using local AI models and tools.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Gather, analyze, and translate business requirements into scalable AI and Agentic AI solutions.
 </li>
 <li>
 Design, develop, test, deploy, and maintain AI-powered applications and autonomous AI agents.
 </li>
 <li>
 Build Agentic AI workflows capable of reasoning, planning, task execution, and orchestration.
 </li>
 <li>
 Develop and integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent architectures.
 </li>
 <li>
 Integrate AI solutions with enterprise applications, APIs, databases, cloud services, and on-premises systems.
 </li>
 <li>
 Optimize AI models, prompts, workflows, and application performance.
 </li>
 <li>
 Ensure AI solutions comply with enterprise security, governance, privacy, and Responsible AI standards.
 </li>
 <li>
 Prepare technical documentation, deployment guides, operational manuals, and knowledge transfer materials.
 </li>
 <li>
 Provide technical support, troubleshooting, performance tuning, and continuous improvements.
 </li>
 <li>
 Design and implement AI solutions using on-premises infrastructure, local models, and enterprise-approved AI tools.
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
 </li>
 <li>
 5+ years of software development experience, including 2+ years working with AI/Generative AI technologies.
 </li>
 <li>
 Strong experience with Python and AI application development.
 </li>
 <li>
 Hands-on experience with Large Language Models (LLMs) and prompt engineering.
 </li>
 <li>
 Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
 </li>
 <li>
 Experience building Agentic AI or multi-agent systems.
 </li>
 <li>
 Experience integrating AI applications with REST APIs, databases, and enterprise applications.
 </li>
 <li>
 Familiarity with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar.
 </li>
 <li>
 Experience working with vector databases such as Milvus, ChromaDB, FAISS, Pinecone, or equivalent.
 </li>
 <li>
 Knowledge of model deployment using Ollama, vLLM, Hugging Face Transformers, NVIDIA NIM, or similar on-premises inference platforms.
 </li>
 <li>
 Experience deploying and managing AI solutions in on-premises environments.
 </li>
 <li>
 Understanding of AI security, governance, privacy, and Responsible AI principles.
 </li>
 <li>
 Experience with Docker, Kubernetes, Git, and CI/CD pipelines is preferred.
 </li>
 <li>
 Strong analytical, problem-solving, communication, and documentation skills.
 
 
 </li>
 </ul>
 
 Preferred Skills
 
 
 
 <ul>
 <li>
 Experience with open-source LLMs such as Llama, Mistral, Qwen, Gemma, or DeepSeek.
 </li>
 <li>
 Experience with GPU infrastructure and AI model optimization.
 </li>
 <li>
 Knowledge of MLOps practices and AI monitoring.
 </li>
 <li>
 Experience integrating AI solutions with enterprise platforms such as SAP, ServiceNow, Microsoft 365, or other enterprise systems.
 </li>
 <li>
 Experience working in highly secure or regulated enterprise environments.
 
 
 </li>
 </ul>
 This JD is aligned with your requirement that
 
 all AI use cases must run on-premises using local models and on-premises tools
 
 , making it suitable for enterprise or government projects.
</div>

AI Engineer / Agentic AI Developer

Booz Allen Hamilton

<div>
 
 Job Number: R0234847
 
 
 
 Agentic AI and Data Engineer
 
 
 
 The Opportunity:
 
 
 
 As an experienced engineer, you know how to design, develop, and deliver production-grade agentic AI systems that demonstrate the practical value of generative AI, large language models (LLMs), and autonomous workflows. This role combines deep technical expertise with strong product skills to design AI applications that leverage prompting, retrieval-augmented generation (RAG), agentic orchestration, evaluation pipelines, and human-in-the-loop systems to deliver measurable impact.
 
 
 You will architect modular, reusable AI application patterns, integrate multiple model providers such as cloud-hosted, local, and hybrid, and apply modern GenAI stack capabilities, including structured prompting, tool use, workflow orchestration, and multi-modal reasoning. You will design solutions deployable across various contexts, from cloud-hosted platforms to portable, self-contained builds, optimizing for latency, cost efficiency, observability, and safety. You will rapidly prototype and iterate using AI-assisted development tools, validating hypotheses through eval-driven development and continuous experimentation.
 
 
 In this role, you&rsquo;ll define the direction of mission-critical agentic systems by selecting and combining prompting strategies, RAG architectures, agentic workflows, and fine-tuned or foundation models as appropriate. You&rsquo;ll be part of a large community of AI and ML engineers across the company, collaborating with data engineers, data scientists, solutions architects, and product owners to deliver world-class solutions.
 
 
 
 What You&rsquo;ll Do:
 
 
 
 <ul>
 <li>
 Design adaptable agentic AI architectures that support multiple model providers, tool ecosystems, modalities, and deployment modes.
 </li>
 <li>
 Build modular and reusable components for prompting, retrieval, orchestration, tool execution, memory management, and evaluation to enable rapid development of new AI capabilities.
 </li>
 <li>
 Integrate LLMs, embeddings, RAG pipelines, structured outputs, and long-context or memory mechanisms into production-ready systems.
 </li>
 <li>
 Apply advanced prompting techniques such as few-shot, chain-of-thought, tool-calling, and function-calling, orchestration frameworks such as LangChain or equivalent, and agentic architectures such as MCP, A2A, or similar patterns, to enable goal-directed autonomy with guardrails, observability, and human oversight, including planning, tool use, delegation, and recovery from failure.
 </li>
 <li>
 Design and implement evaluation frameworks, both offline and online, to measure correctness, robustness, safety, and business impact of AI systems.
 </li>
 <li>
 Optimize models and workflows for cost, latency, reliability, and scalability, using systematic benchmarking and experimentation.
 </li>
 <li>
 Develop data pipelines for ingestion, cleaning, chunking, embedding, indexing, and continuous refresh of structured and unstructured data for RAG and memory systems.
 </li>
 <li>
 Combine text, audio, vision, and other modalities in unified processing workflows, including document understanding, transcription, summarization, and cross-modal reasoning.
 </li>
 <li>
 Leverage vector databases, hybrid search, reranking, and retrieval optimization techniques to enhance grounding and reduce hallucination in RAG systems.
 </li>
 <li>
 Incorporate guardrails, safety filters, access controls, and monitoring mechanisms to ensure responsible and secure deployment of agentic AI systems.
 </li>
 <li>
 Deploy AI services securely and at scale on AWS or equivalent cloud platforms.
 </li>
 <li>
 Use containerizing, including in Docker or Kubernetes, or serverless approaches for flexible deployment.
 </li>
 <li>
 Apply CI/CD and eval-driven development best practices for AI systems, including automated testing of prompts and workflows, versioning of prompts and agents, and safe rollout of model updates.
 </li>
 <li>
 Use asynchronous programming and event-driven patterns to support scalable, long-running, or multi-agent workflows.
 </li>
 <li>
 Leverage modern build and packaging workflows to deliver optimized, portable application artifacts.
 </li>
 <li>
 Use AI assistance tools to accelerate development, debugging, and system design while maintaining engineering rigor and code quality.
 </li>
 <li>
 Collaborate with clients to identify high-value AI opportunities and define solution requirements.
 </li>
 <li>
 Present AI capabilities and technical solutions to both technical and non-technical stakeholders.
 </li>
 <li>
 Lead workshops and prototyping sessions to accelerate adoption.
 </li>
 <li>
 Provide guidance on responsible AI practices, ethics, and compliance.
 
 
 
 </li>
 </ul>
 Join us. The world can&rsquo;t wait.
 
 
 
 You Have:
 
 
 
 <ul>
 <li>
 2+ years of experience with software engineering
 </li>
 <li>
 2+ years of experience in AI or ML-focused roles in a professional work environment
 </li>
 <li>
 Experience with an object-oriented programming language such as Python, and applying it to AI/ML solution development
 </li>
 <li>
 Experience designing and implementing production-grade generative or agentic AI applications
 </li>
 <li>
 Experience with AI orchestration frameworks such as LangChain, agent workflows, tool integration, and multi-provider model integration
 </li>
 <li>
 Experience with RAG architectures, evaluation methodologies, experimentation workflows, and asynchronous or event-driven programming patterns
 </li>
 <li>
 Knowledge of data processing techniques for AI, including text, audio, and multi-modal
 </li>
 <li>
 Ability to obtain a Secret clearance
 </li>
 <li>
 Bachelor&rsquo;s degree in a CS or Engineering field
 
 
 
 </li>
 </ul>
 
 Nice If You Have:
 
 
 
 <ul>
 <li>
 Experience with agent frameworks, interoperability standards, and multi-agent patterns such as MCP, A2A, LangGraph, or equivalent
 </li>
 <li>
 Experience with model fine-tuning, prompt tuning, domain adaptation, or reinforcement learning from human or AI feedback
 </li>
 <li>
 Experience designing evaluation suites or safety testing frameworks for AI systems, and integrating AI systems with external tools, APIs, or enterprise systems via tool-calling or computer-use patterns
 </li>
 <li>
 Experience delivering AI solutions in client-facing engagements
 </li>
 <li>
 Experience with modern front-end libraries and frameworks for component-based UI development, including React, and with workflows such as build pipelines, automated testing, and code quality tooling
 </li>
 <li>
 Experience with in-browser or edge AI execution and performance optimization techniques, as well as modern build and packaging approaches for portable or offline-capable applications
 </li>
 <li>
 Experience with developer productivity tools such as Cursor and Windsurf
 </li>
 <li>
 Secret clearance
 </li>
 <li>
 Master&rsquo;s degree in CS, AI, or a related field
 </li>
 <li>
 AWS Machine Learning, Data Engineer, or Solutions Architect Certification
 
 
 
 </li>
 </ul>
 
 Clearance:
 
 
 
 Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.
 
 
 
 Compensation
 
 
 
 At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen&rsquo;s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
 
 
 Salary at Booz Allen is determined by various factors, including but not limited to location, the individual&rsquo;s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $99,000.00 to $225,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen&rsquo;s total compensation package for employees. This posting will close within 90 days from the Posting Date.
 
 
 
 Identity Statement
 
 
 
 As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
 
 
 
 Candidate AI Usage Policy
 
 
 
 AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on
 
 your
 
 own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited
 
 unless permission is explicitly provided
 
 .
 
 
 
 Work Model
 
 
 
 Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.
 
 
 <ul>
 <li>
 Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
 </li>
 <li>
 Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
 </li>
 <li>
 Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.
 
 
 
 </li>
 </ul>
 
 Commitment to Non-Discrimination
 
 
 
 All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.
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