ai inference engineer

Exxeta

<div><div><div><div><div><div><p>Frankfurt am Main, Karlsruhe, Leipzig, Mannheim, Stuttgart</p>
</div></div><div><p><b>Als AI Inference Engineer baust du die technische Grundlage f&uuml;r produktive AI-Systeme in regulierten Umgebungen. Du entwickelst und betreibst LLM-Inferenzplattformen, die on-premises oder in privaten Cloud-Umgebungen laufen &ndash; sicher, skalierbar, beobachtbar und wirtschaftlich.</b></p>
<p><b>Du sorgst daf&uuml;r, dass moderne Modelle nicht nur in einer Demo &uuml;berzeugen, sondern unter realen Produktionsbedingungen zuverl&auml;ssig funktionieren: mit sauberer GPU-Planung, niedriger Latenz, kontrollierten Kosten, belastbarem Monitoring und klar definierten Betriebsmodellen.</b></p>
</div><h3 class="jobSectionHeader"><b>Was erwartet dich</b></h3>
<div><div><ul><li>Du konzipierst, entwickelst und betreibst produktive LLM-Inferenzplattformen f&uuml;r Kunden mit hohen Anforderungen an Datensouver&auml;nit&auml;t, Sicherheit und Betriebskontrolle &ndash; on-premises, in privaten Cloud-Umgebungen oder souver&auml;nen europ&auml;ischen Cloud-Setups.</li>
<li>Gemeinsam mit Cloud-, Plattform-, Security- und Data-Engineering-Teams sowie unseren Kunden &uuml;berf&uuml;hrst du AI-Use-Cases in den produktiven Betrieb.</li>
<li>Dabei integrierst du moderne Inferenz-Engines und Open-Weights-Modelle in Kubernetes-, Container- und Plattformumgebungen.</li>
<li>Au&szlig;erdem planst und optimierst du GPU- und Speicherressourcen sowie Inferenz-Workloads: Von Modellgr&ouml;&szlig;en, Quantisierung und Batching bis hin zu KV-Cache-Strategien, Latenz, Durchsatz und Kosten.</li>
<li>Du verantwortest die Runtime produktiver AI-Systeme, inklusive Modellserving, APIs, Authentifizierung, Secrets, Observability, Logging</li>
<li>Aus Kundenprojekten entwickelst du wiederverwendbare Referenzarchitekturen, Deployment-Templates und Betriebs-Playbooks und st&auml;rkst so unsere Applied-AI-Capability.</li>
</ul></div></div><h3 class="jobSectionHeader"><b>Was erwarten wir von dir</b></h3>
<div><div><ul><li><b>Pers&ouml;nlicher Background:</b> Erfahrung in Platform Engineering, Cloud Infrastructure, MLOps, LLMOps, DevOps, Backend Engineering oder Machine Learning Engineering. Entscheidend ist deine Erfahrung im Aufbau und Betrieb produktiver Systeme und dein Antrieb zu schneller pers&ouml;nlicher Weiterentwicklung</li>
<li><b>Inference Engineering:</b> Du verstehst die technischen und wirtschaftlichen Zusammenh&auml;nge moderner LLM-Inferenz, von Model-Serving und GPU-Auslastung &uuml;ber Quantisierung, Batching und KV-Cache-Management bis hin zu Latenz, Durchsatz und Kosten.</li>
<li><b>Cloud &amp; Plattformen:</b> Docker, Kubernetes, Helm, Terraform, CI/CD, Linux sowie Observability geh&ouml;ren f&uuml;r dich zum Arbeitsalltag.</li>
<li><b>AI-Verst&auml;ndnis: </b>Du kannst Transformer-basierte Modelle wie LLMs und Embeddings einordnen und fundierte technische Entscheidungen f&uuml;r produktive AI-Systeme treffen.</li>
<li><b>Security &amp; Governance:</b> Themen wie Identit&auml;ten, Berechtigungen, Secrets, Logging, Auditierung und Compliance denkst du, insbesondere in regulierten Umgebungen, von Anfang an mit.</li>
<li><b>Kommunikation &amp; Arbeitsweise: </b>Du vermittelst komplexe technische Zusammenh&auml;nge verst&auml;ndlich, arbeitest pragmatisch und bewegst dich auch in dynamischen Projektumfeldern sicher.</li>
<li><b>Pluspunkt:</b> Erfahrung mit vLLM, SGLang oder vergleichbaren Inference-Technologien, GPU-Clustern, souver&auml;nen Cloud- oder Private-Cloud-Umgebungen.</li>
<li><b>On the road:</b> Du bist reisebereit und flexibel, unsere Kunden bundesweit vor Ort zu beraten.
</li><li><b>Let's talk: </b>Du sprichst flie&szlig;end Deutsch und Englisch - super, dann findest du dich bei Exxeta bestens zurecht</li>
</ul></div></div><h3 class="jobSectionHeader"><b>Warum Exxeta</b></h3>
<div><div><p>Bei Exxeta entwickeln wir digitale L&ouml;sungen, die wirklich etwas ver&auml;ndern &ndash; in Unternehmen, M&auml;rkten und K&ouml;pfen. &Uuml;ber 1200 Kolleg:innen bringen daf&uuml;r Technologie, Ideen und unterschiedliche Perspektiven zusammen. Was uns antreibt: Neugier, Teamspirit und der Anspruch, echten Impact zu schaffen. Hightech with a heartbeat eben.</p>
<p>Wir sind ein Zuhause f&uuml;r Menschen, die etwas bewegen wollen. Diversit&auml;t und unterschiedliche Perspektiven bereichern unser Team. Haltung, Ideen und Lust aufs Machen - das z&auml;hlt bei uns!</p>
<p><b>Bewirb dich jetzt!</b></p>
</div></div></div></div></div><div><div>Deine Kontaktperson</div>
<div><div><div><div><div><h3 class="jobSectionHeader"><b>Sandra Walter</b></h3>
<div>Senior Talent Acquisition Partner</div></div><p></p><div><div><br>
</div></div></div></div></div></div></div><div><div><div><div><h2 class="jobSectionHeader"><b>Deine Benefits</b></h2>
</div><div><div><div><h3 class="jobSectionHeader"><b>An apple a day &hellip;</b></h3>
<p>Kaffee, Tee, Cola, Obst &ndash; geht aufs Haus</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Einfach mal weg?</b></h3>
<p>Bei uns normal: 30 Tage Urlaub und dein Sabbatical</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Flexibles Arbeiten</b></h3>
<p>Ob B&uuml;ro, Remote oder bei Kunden vor Ort? Deine Arbeitsweise gestaltet sich nach Kundenbedarf und Projektanforderung</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Gut ausgestattet</b></h3>
<p>Bei uns arbeitest du mit moderner Hardware. Extra Hardware leasen? Kein Problem</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Immer mobil</b></h3>
<p>Sportlich unterwegs: Wir bezuschussen dein (E-)Bike</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Lifelong Learning</b></h3>
<p>Workshops &amp; Mentoring: Gemeinsam entwickeln wir uns weiter</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Unvergessliche Events</b></h3>
<p>Unsere Erfolge feiern wir am liebsten als Team</p>
</div></div><div><div><h3 class="jobSectionHeader"><b>Versicherung inklusive</b></h3>
<p>Bis zu 1200 Euro Zuschuss f&uuml;r deine Brille, Z&auml;hne oder Massage</p></div></div></div></div></div></div></div>

AI Inference Engineer (all genders)

d-Matrix

<div><p>At <b>d-Matrix</b>, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of <b>respect and collaboration.</b></p><p>
We value humility and believe in direct communication. Our <b>team is inclusive</b>, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? <b>Together</b>, we can help shape the endless possibilities of AI.</p><p>
D-Matrix Frontier Group sits at the leading edge of what&rsquo;s possible with LLM inference on heterogeneous hardware. Our charter spans the full stack: from pathfinding emerging use cases and novel deployment patterns to deep optimization of inference kernels, to building proof-of-concept systems that showcase D-Matrix&rsquo;s unique computational fabric. We are an applied research and engineering team that moves fast, ships real systems, and works directly with product and hardware teams to shape the roadmap.</p><p>
We build the tools, runtimes, and frameworks that let frontier AI models run efficiently and cost-effectively across heterogeneous deployments &mdash; combining D-Matrix silicon with CPUs, GPUs, and custom accelerators. Our work powers everything from benchmarking and evaluation pipelines to production-grade inference serving.</p><p>
This Role</p><p>
We are hiring end-to-end inference engineers who are comfortable going from a novel research idea to a deployed, optimized system. You will work at every layer of the inference stack &mdash; from kernel-level optimization to distributed orchestration to high-level serving APIs.</p><p>
This role could be a great match for you if you:</p><ul><li>Have deep intuition for modern generative AI architectures and how to squeeze performance out of them at inference time.</li><li>Are familiar with the internals of open-source inference frameworks (vLLM, SGLang, TensorRT-LLM, etc.) and can extend or replace them when needed.</li><li>Enjoy pathfinding new use cases &mdash; exploring heterogeneous deployment topologies and building early-stage POCs that prove out new ideas.</li><li>Are results-oriented with a strong bias toward action; you own problems end-to-end from prototype to optimization to handoff.</li><li>Are energized by working at the intersection of novel hardware and frontier models, and want your work to directly influence how next-generation AI silicon is used.</li><li>Value clear communication and thrive in a small, high-ownership team environment.</li></ul><p>
Responsibilities</p><ul><li>Identify and prototype emerging LLM inference use cases suited to heterogeneous hardware deployments.</li><li>Build compelling proof-of-concept systems that demonstrate D-Matrix capabilities to customers, partners, and internal stakeholders.</li><li>Develop and tune custom kernels and operator-level optimizations to maximize throughput and minimize latency.</li><li>Drive quantization, sparsity, and batching strategies tailored to D-Matrix computational model.</li><li>Build and maintain inference runtimes, serving frameworks, and evaluation tooling.</li><li>Contribute to distributed inference systems: tensor/pipeline parallelism, disaggregated prefill/decode, KV-cache management.</li><li>Work closely with hardware architects to provide firmware and compiler teams with actionable inference workload insights.</li><li>Partner with product and business development to translate POCs into customer-facing demonstrations.</li><li>Contribute to technical publications, whitepapers, and open-source projects that advance D-Matrix visibility.</li></ul><p>
Required Qualifications</p><p></p><ul><li>Bachelor&rsquo;s degree in Computer Science, Electrical Engineering, or a related field, and 10+ years of relevant engineering experience; or equivalent demonstrated experience.</li><li>Master&rsquo;s or PhD in Computer Science, Electrical Engineering, or a related field preferred, with 6+ years of relevant industry experience.</li><li>Strong proficiency in Python and C/C++.</li><li>Hands-on experience optimizing LLM inference &mdash; attention kernels, KV cache, batching strategies, quantization (INT8/FP8/INT4).</li><li>Experience with at least one major inference framework (vLLM, SGLang, TensorRT-LLM, ONNX Runtime, or similar) at a contributor level.</li><li>Familiarity with GPU kernel programming (CUDA/Triton) and performance profiling tools.</li></ul><p>
Preferred Qualifications</p><ul><li>Experience with heterogeneous compute deployments &mdash; scheduling inference workloads across dissimilar hardware (accelerators, CPUs, GPUs).</li><li>Familiarity with custom silicon or ASIC-based inference (beyond GPU-only environments).</li><li>Experience with distributed inference: tensor parallelism, pipeline parallelism, disaggregated serving.</li><li>Contributions to open-source inference or ML systems projects.</li><li>Experience with production inference serving at scale (latency SLOs, continuous batching, multi-model serving).</li><li>Familiarity with speculative decoding, mixture-of-experts routing, or long-context serving techniques.</li><li>Working familiarity with the material in the JAX Scaling Book or equivalent systems-level understanding of modern LLM training and inference.</li></ul><p>
Why D-Matrix Frontier Group</p><ul><li>Work on genuinely novel hardware &mdash; D-Matrix in-memory compute architecture opens up inference optimization problems that don&rsquo;t exist anywhere else.</li><li>End-to-end ownership from idea to deployed system, with a short feedback loop between your work and real hardware.</li><li>Small, senior team with high autonomy and direct influence on product direction.</li><li>Competitive compensation, equity, and benefits in Santa Clara, CA.</li></ul><div></div><p><b><br>
Equal Opportunity Employment Policy</b></p><p><b>
d-Matrix</b> is proud to be an equal opportunity workplace and affirmative action employer. We&rsquo;re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.</p><p><b>
d-Matrix</b> does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
</p><p>Compensation Range: $195K - $285K</p></div>

Principal LLM Inference Engineer

CRUTZ LEELA ENTERPRISES

<p>OVERVIEW</p><p>We are hiring an ML Systems Engineer to design and deliver cutting-edge AI solutions for enterprise</p><p>clients at the frontier of agentic AI, inference engineering, and ML systems architecture. You will go</p><p>beyond applied ML - dissecting how AI systems are built, optimized, and scaled - designing production-</p><p>grade architectures spanning retrieval systems, inference pipelines, and agentic workflows. You will</p><p>translate state-of-the-art capabilities into robust, performant solutions, operating at the intersection of ML</p><p>research awareness and engineering discipline.</p><p>KEY RESPONSIBILITIES</p><ul><li>Design and deliver production-grade AI systems for enterprise clients spanning agentic workflows,</li></ul><p>LLM inference pipelines, and retrieval-augmented architectures.</p><ul><li>Lead ML systems architecture decisions - model serving topology, inference backend selection, KV</li></ul><p>cache management, batching strategies, and memory optimization - alongside ML performance</p><p>engineering to profile bottlenecks, benchmark throughput/latency, and evaluate quantization</p><p>strategies (GPTQ, AWQ, GGUF).</p><ul><li>Architect RAG pipelines and agentic AI systems - from chunking, embedding, hybrid retrieval, and re-</li></ul><p>ranking through to multi-agent orchestration, tool use, and memory architectures.</p><ul><li>Evaluate frontier model capabilities - reasoning models, multimodal systems, fine-tuned variants - and</li></ul><p>make principled architectural trade-off decisions for client contexts.</p><ul><li>Build reusable accelerators, reference implementations, and evaluation/observability frameworks</li></ul><p>encoding best practices across engagements.</p><ul><li>Contribute to technical solutioning - architecture designs, proof-of-concepts, and feasibility</li></ul><p>assessments - in client-facing contexts.</p><p>TECHNICAL QUALIFICATIONS</p><p>Core Requirements</p><ul><li>Python &amp;amp; ML ecosystem: Strong programming skills with production AI system experience; hands-</li></ul><p>on with the PyTorch ecosystem including Hugging Face Transformers, PEFT, Accelerate, and</p><p>Datasets.</p><ul><li>LLM inference &amp;amp; serving: Deep knowledge of KV cache mechanics, quantization, and batching;</li></ul><p>hands-on with at least one inference runtime (vLLM, TGI, TensorRT-LLM, SGLang, or similar).</p><ul><li>Hands-on experience supporting AI/ML and LLM inference platforms at scale, including</li></ul><p>working with vLLM for high-performance LLM serving, optimization, and large-scale inference.</p><ul><li>RAG &amp;amp; Agentic Systems: Experience designing retrieval architectures and building agentic systems</li></ul><p>using LangGraph, LlamaIndex Workflows, AutoGen, or CrewAI - including tool use, memory, and</p><p>multi-agent coordination.</p><ul><li>LLM APIs &amp;amp; prompt engineering: Strong grasp of structured output generation, function calling, and</li></ul><p>provider SDK usage across OpenAI, Anthropic, Mistral, Hugging Face, and similar.</p><ul><li>Deployment fundamentals: Proficiency with Docker, containerization, and Linux environments for</li></ul><p>packaging, deploying, and debugging AI systems.</p><ul><li>Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring,</li></ul><p>and productivity enhancement.</p><p>Preferred</p><ul><li>Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding</li></ul><p>of when fine-tuning is the right lever vs. prompting or RAG.</p><ul><li>Low-level AI systems: Familiarity with CUDA, Triton, or similar GPU programming models; working</li></ul><p>knowledge of C++ or Rust.</p><ul><li>Infrastructure &amp;amp; observability: Kubernetes for containerized AI workloads; experience with</li></ul><p>LangSmith, Arize, W&amp;amp;B, Phoenix, or Prometheus/Grafana for ML observability.</p><p>WAYS TO STAND OUT FROM THE CROWD</p><ul><li>You have built and deployed a production agentic system and can speak to the failure modes and</li></ul><p>design decisions that only emerge at runtime.</p><ul><li>You have done inference optimization at a systems level - tuning serving infrastructure, implementing</li></ul><p>custom batching logic, or optimizing a quantization pipeline to hit real SLAs.</p><ul><li>You have open-source contributions to prominent ML systems repositories - vLLM, SGLang,</li></ul><p>llama.cpp, TGI, LangChain, LlamaIndex, or similar - demonstrating work that holds up to community</p><p>scrutiny.</p><ul><li>You have designed custom LLM evaluation frameworks with structured regression harnesses,</li></ul><p>domain-specific evals, or human-in-the-loop feedback loops - beyond off-the-shelf metrics.</p><ul><li>You bring a client-facing engineering mindset and can defend opinions on reasoning models, long-</li></ul><p>context retrieval, or inference hardware tradeoffs based on hands-on experimentation.</p><p>Pay: ₹500,000.00 - ₹1,800,000.00 per year</p><p>Benefits:</p><ul><li>Provident Fund</li></ul><p>Work Location: In person</p>

ML Systems Engineer (Agentic AI & Inference Engineering)

Roblox

<div>San Mateo, CA, United StatesEarly CareerID: 6400</div>
<br><p></p><div><div><div><p>Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences&ndash; all created by our global community of developers and creators.
</p><p>At Roblox, we&rsquo;re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.<b> </b>We&rsquo;re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.
</p><p>A career at Roblox means you&rsquo;ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.</p></div><p><b>
Team</b></p>
<ul>
<li><b>Creator Services Machine Intelligence Team</b>: The Machine Intelligence team is building an NPC system that can (1) play any Roblox game and (2) perform real-time inference efficiently enough to support deployment to all Roblox players.
</li><li><b>ML Platform Team</b>: The Foundation AI Group is on a mission to establish Roblox as the standard for 3D foundational models (3DFMs), democratizing creation by making it simple for anyone to generate high-quality, immersive 3D experiences using AI. The AI Platform team is a foundational part of this vision, supporting hundreds of ML use cases and billions of inferences daily across Discovery, Safety, Engine, and more. We are seeking exceptional PhD new graduates to drive innovation across three critical areas: <b>AI Platform, Distributed Inference Systems.</b></li>
</ul>
<p><b>What You Will Do</b></p>
<p>
As a Senior Machine Learning Engineer, you will be a key contributor to building the cutting-edge systems that power AI at Roblox.
</p><p><b>Creator Services Machine Intelligence Team</b></p>
<ul>
<li><b>Develop Scale Data Pipelines:</b> Design, build and maintain robust data pipelines to collect complex 3D game states and real-time player actions across the platform.</li>
<li><b>Train Novel Architectures:</b> Solve the feature extraction across games for NPC model in a general and scalable way and drive model training speed for novel, sophisticated deep learning architectures.</li>
<li><b>Optimize Real-Time Inference:</b> Engineer high-performance model inference solutions to support the seamless deployment of 10s to 100s of autonomous NPCs in real-time environments.</li>
</ul>
<p><b>ML Platform Team</b></p>
<p><b>
Track 1: AI Platform Projects</b></p>
<ul>
<li>Pioneer next-generation AI tooling to enhance the efficiency, cost, and usability of ML@Roblox.</li>
<li>Build and maintain core platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.</li>
<li>Design great developer experiences (paved-road templates, tooling, visualizations) to reduce time-to-production and ensure foundational AI systems are scalable and reliable.</li>
</ul>
<p><b>Track 2: Distributed Inference &amp; Systems Optimization</b></p>
<ul>
<li>Architect and implement scalable distributed inference systems for efficiently serving LLMs and Large Recommender Models at massive scale.</li>
<li>Optimize our inference engine to serve millions of QPS at low latency.</li>
<li>Conduct deep, low-level performance analysis and optimize ML models (using techniques like continuous batching, speculative decoding, and quantization) and systems on GPU architectures to maintain peak performance and stability.</li>
</ul>
<p><b>You Have</b></p>
<ul>
<li>Possessing or pursuing a Ph.D. in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field, with a thesis aligned to Roblox&rsquo;s research areas.</li>
<li>Built end-to-end ML pipelines and managed model inference and deployment.</li>
<li>Experience with novel datasets, and building real-world agentic applications.</li>
<li>Scaled high-performance, high-availability architectures.</li>
<li>Handled<b> </b>infrastructure using Kubernetes and major cloud providers (AWS, Azure, or GCP).</li>
</ul><div><div><div><p>
For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on <b>this page</b>.</p></div><div>
Annual Salary Range</div><div>
$196,750&mdash;$243,290 USD</div></div></div><div><p>
Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).</p>
<p>
Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.</p>
<p>
For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.</p></div></div></div>

[2026] Senior Machine Learning Engineer (Systems), Embodied AI/NPCs, ML Platform - PhD Early Career

HCLTech

Bengaluru, Karnataka
<br>Job Summary
<br>AI Engineer (LLM / Generative AI)
<br>Role Summary
<br><br>
The AI Engineer will design, build, deploy, and operate production-grade Large Language Model (LLM) solutions. This role focuses on engineering rigor—turning LLM capabilities into reliable, scalable, and secure enterprise applications, and continuously improving them based on performance, cost, and user feedback.
<br><br>
Key Responsibilities
<br><br>
<ul>
<li>Design and build LLM-powered applications using proprietary and open-source models.</li>
<li>Implement prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling, and agent workflows.</li>
<li>Deploy LLM solutions into cloud and enterprise environments with scalability and reliability.</li>
<li>Build inference APIs, microservices, and CI/CD pipelines for AI applications.</li>
<li>Monitor model quality, latency, cost, drift, and hallucinations in production.</li>
<li>Fine-tune and enhance models using parameter-efficient techniques where required.</li>
<li>Optimize inference performance using caching, batching, quantization, and prompt optimization.</li>
<li>Ensure security, privacy, and responsible AI guardrails in all deployments.</li>
<li>Collaborate with product, platform, and engineering teams to deliver enterprise AI solutions.</li>
</ul>
Required Skills & Experience
<br><br>
<ul>
<li>Strong programming skills in Python; experience with backend APIs.</li>
<li>Hands-on experience with LLM frameworks (LangChain, LlamaIndex, or equivalent).</li>
<li>Experience building RAG pipelines using vector databases.</li>
<li>Knowledge of Docker, Kubernetes, cloud platforms, and CI/CD pipelines.</li>
<li>Familiarity with LLMOps / MLOps tools and monitoring systems.</li>
</ul>
Key Responsibilities
<br>Experience Level
<br><br>
<ul>
<li>3–5 years of overall software or ML engineering experience.</li>
<li>1–3+ years of hands-on experience delivering LLM or Generative AI solutions in production.</li>
</ul>
Success Expectations
<br><br>
<ul>
<li>Production-grade LLM solutions running reliably at scale.</li>
<li>Continuous improvement in quality, cost-effectiveness, and user outcomes.</li>
<li>Reusable AI engineering patterns that accelerate enterprise AI adoption.</li>
</ul>
<b>#body.unify div.unify-button-container .unify-apply-now:</b> focus, #body.unify div.unify-button-container .unify-apply-#body.unify div.unify-button-container .unify-apply-now: focus, #body.unify div.unify-button-container .unify-apply-

Senior Developer

hatch IT

<div><p>hatch I.T. is partnering with Expression to find an<b> AI/ML Engineer</b>. See details below:</p>
<p><b>
About The Role:</b></p>
<p>
Expression is seeking an experienced AI/ML Engineer to design, optimize, and evaluate machine learning capabilities that operate efficiently on heterogeneous edge computing platforms. Working closely with software engineers, you will develop AI pipelines that enable intelligent signal characterization, data prioritization, distributed inference, and decision support while operating within constrained compute, bandwidth, and communications environments.</p>
<p>
The ideal candidate has strong expertise in applied machine learning, modern LLM technologies, edge AI optimization, and production deployment of AI systems.</p>
<p><b>
Security Clearance</b>: Eligible to obtain Secret or Top Secret Clearance (U.S citizenship required)</p>
<p><b>
About the Company:</b></p>
<p>
Founded in 1997 and headquartered in Washington DC, Expression provides data fusion, data analytics, software engineering, information technology, and electromagnetic spectrum management solutions to the U.S. Department of Defense, Department of State, and national security community. Expression&rsquo;s &ldquo;Perpetual Innovation&rdquo; culture focuses on creating immediate and sustainable value for their clients via agile delivery of tailored solutions built through constant engagement with their clients. Expression was ranked #1 on the Washington Technology 2018's Fast 50 list of fastest growing small business Government contractors and a Top 20 Big Data Solutions Provider by CIO Review.</p>
<h3 class="jobSectionHeader"><b>
Responsibilities:</b></h3><div>
<ul><li>Design and implement AI capabilities supporting intelligent data characterization, classification, prioritization, and decision support.</li>
<li>Evaluate, optimize, and deploy open-weight foundation models appropriate for resource-constrained edge environments.</li>
<li>Develop efficient inference pipelines supporting heterogeneous compute environments ranging from embedded processors to workstation-class systems.</li>
<li>Implement Retrieval-Augmented Generation (RAG), semantic search, and knowledge retrieval capabilities where appropriate.</li>
<li>Design AI orchestration workflows supporting distributed inference across multiple edge devices.</li>
<li>Develop evaluation methodologies for AI accuracy, latency, resource utilization, and operational performance.</li>
<li>Implement model monitoring, observability, testing, and automated evaluation frameworks.</li>
<li>Collaborate with software engineers to integrate AI models into production software platforms.</li>
<li>Optimize models using quantization, pruning, distillation deployment technologies.</li>
<li>Support experimentation involving multimodal data sources, sensor-derived features, and structured mission data.</li>
<li>Develop AI governance practices including model evaluation, explainability, responsible AI, and secure deployment.</li>
<li>Document model development, evaluation results, and technical recommendations.</li>
<li>Support customer demonstrations and prototype evaluations.</li>
</ul></div><h3 class="jobSectionHeader"><b>Qualifications:</b></h3><div>
<ul><li>Bachelor degree in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, Applied Mathematics, or related discipline. An advanced degree is preferred.</li>
<li>5-8+ years of professional experience developing production AI or machine learning applications.</li>
<li>Strong Python programming experience.</li>
<li>Experience with PyTorch.</li>
<li>Experience deploying LLMs in production environments.</li>
<li>Experience with LangGraph, LangChain, CrewAI, Semantic Kernel, or similar orchestration frameworks.</li>
<li>Experience implementing Retrieval-Augmented Generation (RAG).</li>
<li>Experience with vector databases and semantic search.</li>
<li>Experience deploying AI models on edge or resource-constrained devices.</li>
<li>Experience with model optimization techniques including quantization, model compression, or inference acceleration.</li>
<li>Experience designing evaluation frameworks for AI systems.</li>
<li>Experience with Docker and cloud-native AI deployment.</li>
<li>Excellent communication and collaboration skills.</li>
</ul></div><h3 class="jobSectionHeader"><b>Preferred Qualifications:
</b></h3><div><ul><li>Experience applying AI to sensor analytics, time-series data, or signal processing.</li>
<li>Experience with software-defined radio data, RF analytics, or geospatial data analytics.</li>
<li>Experience developing multimodal AI applications.</li>
<li>Experience deploying AI across distributed edge computing environments..</li>
<li>Experience supporting DoD, Intelligence Community, or Federal customers.</li>
<li>Experience working in bandwidth-constrained or disconnected operational environments.</li>
<li>Experience supporting National Security or Federal Civilian customers.</li>
</ul>
</div><div><div><p><b>Benefits:</b></p>
<p>
Expression offers competitive salaries and benefits, such as:</p>
<ul>
<li>401k matching</li>
<li>PPO and HDHP medical/dental/vision insurance</li>
<li>Education reimbursement up to $10,000/yr</li>
<li>Complimentary life insurance</li>
<li>Generous PTO and 11 days of holiday leave</li>
<li>Onsite gym facility and trainer</li>
<li>Commuter Benefits Plan</li>
<li>In-office Cold Brew Coffee</li>
</ul>
</div></div><p>We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.</p></div>

AI/ML Engineer

Dautom

<div>
 <p>
  The AI Infrastructure Engineer is a platform specialist responsible for architecting, building, and operating high-performance AI infrastructure to support advanced AI workloads, including LLMs, GenAI, Computer Vision, and MLOps. This role will focus on managing GPU clusters (NVIDIA A100/H100), deploying and maintaining Red Hat OpenShift AI (RHODS), and ensuring secure, scalable, and cost-efficient AI platforms across SDD&rsquo;s Sovereign Cloud and hybrid/multi-cloud environments. The engineer will enable enterprise-grade AI adoption for 200+ government entities.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Key Responsibilities &amp; Deliverables
 </p>
 <p>
  <br/>
 </p>
 <p>
  GPU &amp; AI Platform Architecture
 </p>
 <p>
  Design and implement GPU-based compute clusters. Define reference architectures for LLM hosting, Vector Databases, MLOps, and high-performance storage/networking.
 </p>
 <p>
  Fully operational GPU-based AI infrastructure. GPU Cluster Uptime and Performance Utilization. Reduction in Cost per Training/Inference Workload.
 </p>
 <p>
  GPU Cluster Operations
 </p>
 <p>
  Install, configure, and optimize core components: CUDA, cuDNN, NCCL, NVIDIA Drivers, and GPU Operators. Implement GPU partitioning, scheduling, and performance tuning for high-end GPUs (e.g., A100/H100).
 </p>
 <p>
  High-availability architecture for all AI workloads. Complete documentation and runbooks.
 </p>
 <p>
  OpenShift AI (RHODS) Management
 </p>
 <p>
  Deploy, configure, and maintain the Red Hat OpenShift AI (RHODS) platform for multi-tenant use. Manage the integration of NVIDIA GPU Operator for efficient GPU scheduling and support Data Scientists with Notebooks, Training, and Inference Endpoints.
 </p>
 <p>
  Production-ready OpenShift AI (RHODS) platform. AI Project Onboarding Speed.
 </p>
 <p>
  LLM &amp; Model Serving
 </p>
 <p>
  Build and manage infrastructure for hosting and serving open-source LLM frameworks (Llama, Falcon, Mistral) and supporting RAG pipelines, LoRA adapters, and Vector Databases (Milvus, pgvector).
 </p>
 <p>
  Multi-model LLM serving environment for entities. MLOps Pipeline Success Rate and Deployment Frequency.
 </p>
 <p>
  MLOps &amp; Automation
 </p>
 <p>
  Implement IaC (Terraform, Ansible) and GitOps for the automated lifecycle management of the AI platform (node onboarding, scaling, model rollout/rollback). Build robust MLOps pipelines for data prep, training, evaluation, and monitoring (using tools like MLflow/Kubeflow).
 </p>
 <p>
  Infrastructure automation via Terraform &amp; Ansible. Automation Coverage for AI Infrastructure.
 </p>
 <p>
  <br/>
 </p>
 <p>
  Required Qualifications &amp; Experience
 </p>
 <ul>
  <li>
   Experience: 7&ndash;12 years in Cloud Infrastructure, DevOps, ML Infrastructure, or Platform Engineering.
  </li>
  <li>
   Deep Hands-On Expertise:
  </li>
  <li>
   GPU Systems (NVIDIA A100/H100), Linux, Containers, and Kubernetes.
  </li>
  <li>
   OpenShift AI (RHODS) or equivalent Kubernetes GPU orchestration.
  </li>
  <li>
   LLM Hosting (Llama, Mistral, Falcon, etc.) and supporting Vector Databases/RAG systems.
  </li>
  <li>
   Strong Experience In: TensorFlow, PyTorch, Hugging Face, Distributed Training (DDP, Deep Speed), and ML Ops Stacks (ML flow, Kubeflow).
  </li>
 </ul>
 <p>
  Essential Skills &amp; Competencies
 </p>
 <ul>
  <li>
   Technical: Deep understanding of GPU compute, HPC architectures, and ML performance profiling. Strong skills in IaC (Terraform/Ansible), CI/CD, and OpenShift/Kubernetes operators.
  </li>
  <li>
   Soft Skills: Strong troubleshooting, optimization, and performance engineering mindset. Excellent cross-functional collaboration and documentation skills.
  </li>
 </ul>
 <p>
  Preferred Certifications
 </p>
 <ul>
  <li>
   NVIDIA Deep Learning / AI Infrastructure Certification
  </li>
  <li>
   Red Hat OpenShift AI specialization
  </li>
  <li>
   Kubernetes CKA/CKAD
  </li>
  <li>
   Azure AI or Oracle Cloud AI certifications
  </li>
  <li>
   Terraform &amp; Ansible certifications
  </li>
 </ul>
 <p>
  <br/>
 </p>
</div>


AI Infrastructure Engineer

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