While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi! About Quantiphi: Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the very core of their operations. Since our founding in 2013, Quantiphi has tackled some of the world’s most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value, not just technology for technology’s sake. Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact. We’ve been recognized with: - 17x Google Cloud Partner of the Year awards in the last 8 years. - 3x AWS AI/ML award wins. - 3x NVIDIA Partner of the Year titles. - 2x Snowflake Partner of the Year awards. - We have also garnered top analyst recognitions from Gartner, ISG, and Everest Group. - We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators. - We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023. Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here! For more details, visit: Website or LinkedIn Page. Role: Senior Machine Learning Engineer - Al Platform & Agentic Systems Experience Level: 5-10 yrs Work Location: US East/Canada (Remote) Role Overview: We are seeking a Senior Machine Learning Engineer (MLE) to support a large-scale healthcare modernization initiative, working at the intersection of the Data Platform and AI layer. This role will focus on building and scaling AI model training, tuning, and deployment environments, while enabling advanced agentic workflows and search platforms. You will play a critical role in taking ML/LLM solutions from experimentation to production, primarily within GCP (Vertex AI), with exposure to multi-cloud environments (Azure OpenAI / Azure ML). This is a hands-on, high-impact role ideal for someone with deep experience in LLMs, RAG architectures, and ML platform engineering. Key Responsibilities: ML Platform Engineering (GCP – Vertex AI) - Design and implement end-to-end ML workflows using Vertex AI Pipelines (Kubeflow v2) - Build and optimize model training and tuning pipelines (custom jobs, HPO with Vizier) - Deploy scalable models using Vertex AI Endpoints (online & batch inference) - Optimize models for performance and cost (quantization, ONNX, TensorRT) - Manage model lifecycle including experimentation, versioning, and monitoring LLM Development & Fine-Tuning - Fine-tune LLMs using SFT, RLHF/RLAIF, and parameter-efficient methods (LoRA, QLoRA) - Work with models such as Gemini, PaLM, Llama, Mistral - Build robust evaluation frameworks (BLEU, ROUGE, RAGAS, custom metrics) - Implement A/B testing and experiment tracking (Vertex AI / MLflow) RAG & Search Platform Development - Design and deploy end-to-end RAG pipelines (ingestion, chunking, embeddings, retrieval, reranking) - Build and optimize vector search systems using Vertex AI Vector Search or similar - Develop hybrid search architectures (dense + sparse retrieval, BM25, RRF) - Improve search relevance via query understanding, ranking models, and LLM-based enhancements Agentic AI & Workflow Orchestration - Build multi-step reasoning systems using LangChain, LlamaIndex, LangGraph, CrewAI, or AutoGen - Design and expose agent tools (Text-to-SQL, vector search, APIs, knowledge graph tools) - Implement memory and state management for multi-turn workflows - Develop secure and scalable prompt management and guardrails Data & ML Engineering - Build and maintain training data pipelines including labeling and dataset curation - Leverage Vertex AI Feature Store for feature management - Collaborate closely with data teams on data contracts and schema design MLOps & Productionization - Implement CI/CD pipelines for ML using Cloud Build & Artifact Registry - Manage model registry, deployment gates, and monitoring (drift, performance) - Support distributed training workloads (GPU/TPU, multi-node environments) Healthcare AI Applications - Work with clinical NLP pipelines (NER, entity extraction, classification) - Utilize healthcare data formats such as FHIR and de-identified datasets - Ensure solutions align with healthcare compliance standards (e.g., HIPAA) Basic Qualifications: - 5–10+ years of experience in Machine Learning / AI Engineering - 2+ years of hands-on experience with LLMs / Generative AI in production - Strong expertise in GCP Vertex AI (training, pipelines, deployment) - Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) - Experience building RAG systems or agentic workflows in production - Solid understanding of embeddings, vector databases, and search systems - Experience with API development (FastAPI, gRPC) Other Qualifications (OQs): - Experience with Azure ML / Azure OpenAI in a hybrid cloud environment - Background in Healthcare / Clinical AI (FHIR, NLP, regulated datasets) - Familiarity with distributed training (DeepSpeed, multi-GPU/TPU) - GCP Professional ML Engineer certification - Strong collaboration skills working across data, platform, and business teams - Ability to communicate complex AI concepts to non-technical stakeholders - Self-starter with experience working in client-facing, fast-paced environments - Ability to scope, estimate, and deliver independently What’s in it for YOU at Quantiphi: - Make an impact at one of the world’s fastest-growing AI-first digital engineering companies. - Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues. - Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines. - Stay ahead of the curve, immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

• Lead the development of the BEV model: define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). • Design advanced multimodal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. • Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations. • Own large-scale training workflows, from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization. • Improve model robustness and generalization, accounting for long-tail conditions such as low visibility, occlusions, and rare scene configurations. • Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance. • Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces. • Mentor and guide machine learning engineers, cultivating best practices in experimentation, code quality, and model validation. • Remain at the forefront of machine learning research, exploring self-supervised learning, large-scale pretraining, and foundation models for 3D perception.

Role Description This engagement is focused on building an internal AI platform that enables developers to ship AI-powered services efficiently. The objective is to improve DevEx and reduce time-to-market for AI features. - Build and operate the AI platform infrastructure enabling developers to ship LLM-based services faster. - Implement and maintain Kubernetes-based runtime environments (incl. AKS) for AI workloads. - Manage infrastructure as code with Terraform (modules, environments, CI/CD automation). - Support LLM workflows: RAG, agents, prompt experimentation, evaluations, and deployment patterns. - Integrate and operate tooling such as Azure AI Foundry, LiteLLM, Langfuse, MLflow. - Orchestrate pipelines using Kubeflow Pipelines and/or Argo Workflows (build, deploy, evaluate). - Improve platform reliability and observability (monitoring, logging, tracing, cost/perf signals). - Collaborate closely with developers to streamline DX (APIs, templates, docs, golden paths, automation). Qualifications - Strong hands-on experience with Kubernetes in production (preferably AKS). - Solid Terraform expertise (IaC best practices, multi-env setups). - Practical experience supporting ML/LLM workloads in a platform or DevOps/MLOps context. - Proficiency in Python for automation, scripting, and supporting APIs/evaluation tooling. - Understanding of CI/CD, release processes, and production-grade operations. - Ability to work under tight timelines and deliver pragmatically. Requirements - Experience building internal developer platforms or “paved roads” for engineering teams. - Familiarity with LLM evaluation frameworks, prompt testing workflows, and LLM observability. - Exposure to RAG architectures, vector databases, and agentic patterns. - Experience with Kubeflow, Argo, and ML lifecycle tooling. Engagement Type Long-term B2B contract. Team You will join a team of 5, with 3 AI Platform Engineers being added. Location / Timezone Remote work from Croatia, Poland, Portugal, and Serbia. European working hours. Occasionally available for meetings up to 10:00 AM PST (US overlap).

• Design and implement scalable, secure, and cost‑efficient MLOps solutions leveraging AWS and Databricks. • Automate ML deployment pipelines, reducing manual intervention and operational overhead. • Collaborate closely with data scientists to ensure solutions align with established MLOps architecture, best practices, and platform standards. • Integrate security controls and compliance requirements throughout the entire machine learning lifecycle. • Own and manage incidents end‑to‑end, from root cause analysis to prevention of future occurrences. • Contribute to software system architecture and the design of platform‑level components. • Build and optimize ML training, retraining, and inference pipelines, ensuring reliability and scalability. • Enhance observability with metrics, logging, tracing, and dashboards to ensure system visibility and performance. • Drive best practices in infrastructure automation, CI/CD, and cloud resource management across ML teams.