AVP AI Solutions Management

Senior Data Scientist 2

Senior Machine Learning Engineer (Production & MLOps Focus)

Senior DevOps Engineer

Responsibilities: <ul><li>Support the AI use-case intake process, gather requirements, and rapidly assess strategic fit, value, complexity, and feasibility.</li><li>Maintain an AI use-case/solution inventory; identify duplication and promote reuse of existing platforms and components.</li><li>Evaluate alignment to the company’s AI strategy, enterprise architecture, data strategy, and target operating model.</li><li>Help establish guidelines for build/buy/hybrid and create recommendation artifacts for leadership and governance bodies.</li><li>Assess candidate architectures across cloud services, data platforms, model choices (LLMs, classical ML, CV, NLP), and integration patterns.</li><li>Compare third-party SaaS/managed services with in-house custom builds and hybrids; estimate TCO, scalability, maintainability, and time-to-value.</li><li>Support vendor evaluations and small proof-of-concept efforts, reviewing items like security practices, data handling, SLAs, integration needs, and roadmap alignment.</li><li>Partner with Procurement/Vendor Management on contracting, licensing, data processing, and exit strategies.</li><li>Partner with Engineering, Data Science, and DevOps/MLOps to scope pilots and define success metrics and guardrails.</li><li>Ensure handoffs include documentation, controls, and operational readiness</li><li>Develop playbooks, patterns, and templates to support AI solution reviews, and help enable business and tech teams through training sessions and office hours..</li><li>Track portfolio outcomes (benefits, risk findings, reuse rate) and continuously improve intake and evaluation processes.</li></ul> Requirements: <ul><li>Software engineering and cloud architecture knowledge across AWS/Azure; APIs; event/microservices; observability.</li><li>Good understanding of AI/ML and LLM stacks (vector databases, embeddings, RAG, prompt engineering, guardrails).</li><li>Understanding of privacy/security controls in financial services (data classification, encryption, DLP, IAM, segmentation) and third-party risk.</li><li>Proven ability to translate business objectives into technical evaluation criteria and clear decisions.</li><li>Excellent written and verbal communication for executive-level summaries and deep-dive technical reviews.</li><li>Critical thinker with structured problem-solving and sound judgment</li></ul>

Responsibilities: <ul><li>Lead the design and development of LLM-powered solutions for clinical summarization, intelligent chat, and document understanding to improve patient care outcomes.</li><li>Architect and implement end-to-end machine learning pipelines, including Retrieval-Augmented Generation (RAG) systems using vector databases such as Pinecone and Weaviate.</li><li>Fine-tune and optimize state-of-the-art language models (e.g., GPT, LLaMA, Mistral) and apply advanced prompt engineering to address specialty-specific healthcare use cases.</li><li>Own project delivery end to end, collaborating with stakeholders to ensure AI solutions are ethical, scalable, reliable, and well-governed.</li><li>Mentor and support junior data scientists, fostering a culture of experimentation, learning, and innovation.</li></ul> Requirements: <ul><li>Experience: 8–12 years in Data Science/ML, with at least 3+ years specifically focused on LLMs and Generative AI.&nbsp;</li><li>Technical Mastery: Deep hands-on expertise in Python and frameworks like PyTorch, Hugging Face, LangChain, or LlamaIndex.&nbsp;</li><li>Cloud Fluency: Proven ability to deploy models at scale using AWS SageMaker, Azure ML, or GCP Vertex AI.&nbsp;</li><li>MLOps Mindset: Experience with MLflow, Airflow, Docker, and Kubernetes to ensure our "bold innovations" are also stable and reliable.&nbsp;</li><li>Academic Foundation: A strong grasp of statistical analysis and evaluation techniques to mitigate hallucinations and ensure clinical accuracy.&nbsp;</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: Cloud Infrastructure Design and Deployment: <ul><li>Architect, design, and implement scalable, reliable, and secure solutions on GCP.</li><li>Deploy and manage GCP services in both development and production environments, ensuring seamless integration with existing infrastructure.</li><li>Implement and manage core services such as BigQuery, Datafusion, Cloud Composer (Airflow), Cloud Storage, Data Fusion, Compute Engine, App Engine, Cloud Functions and more.</li></ul> Infrastructure as Code (IaC) and Automation: <ul><li>Develop and maintain infrastructure as code using Terraform or CLI scripts to automate provisioning and configuration of GCP resources.</li><li>Establish and document best practices for IaC to ensure consistent and efficient deployments across environments.</li></ul> DevOps and CI/CD Pipeline Development: <ul><li>Create and manage DevOps pipelines for automated build, test, and release management, integrating with tools such as Jenkins, GitLab CI/CD, or equivalent.</li><li>Work with development and operations teams to optimize deployment workflows, manage application dependencies, and improve delivery speed.</li></ul> Security and IAM Management: <ul><li>Handle user and service account management in Google Cloud IAM.</li><li>Set up and manage Secrets Manager and Cloud Key Management for secure storage of credentials and sensitive information.</li><li>Implement network and data security best practices to ensure compliance and security of cloud resources.</li></ul> Performance Monitoring and Optimization: <ul><li>Monitoring &amp; Security: Set up observability tools like Prometheus, Grafana, and integrate security tools (e.g., SonarQube, Trivy).</li><li>Networking &amp; Storage: Configure DNS, networking, and persistent storage solutions in Kubernetes.</li><li>Set up monitoring and logging (e.g., Cloud Monitoring, Cloud Logging, Error Reporting) to ensure systems perform optimally.</li><li>Troubleshoot and resolve issues related to cloud services and infrastructure as they arise.</li></ul> Workflow Orchestration: <ul><li>Orchestrate complex workflows using Argo Workflow Engine.</li><li>Containerization: Work extensively with Docker for containerization and image management.</li><li>Optimization: Troubleshoot and optimize containerized applications for performance and security.</li></ul> Requirements:﻿ <ul><li>Bachelors or masters degree in computer science, Information Technology, or a similar field</li><li>Must have 10 + years of extensive experience in designing, implementing, and maintaining applications on GCP and OpenShift</li><li>Comprehensive expertise in GCP services such as GKE, Cloudrun, Functions, Cloud SQL, Firestore, Firebase, Apigee, GCP App Engine, Gemini Code Assist, Vertex AI, Spanner, Memorystore, Service Mesh, and Cloud Monitoring</li><li>Solid understanding of cloud security best practices and experience in implementing security controls in GCP</li><li>Thorough understanding of cloud architecture principles and best practices</li><li>Experience with automation and configuration management tools like Terraform and a sound understanding of DevOps principles</li><li>Proven leadership skills and the ability to mentor and guide a technical team</li></ul>

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AVP AI Solutions Management

Senior Data Scientist 2

Senior Machine Learning Engineer (Production & MLOps Focus)

Senior DevOps Engineer

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AVP AI Solutions Management

Senior Data Scientist 2

Senior Machine Learning Engineer (Production & MLOps Focus)

Senior DevOps Engineer

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