USA_Developer

Skills: Category Name Required Importance Experience SkillCategoryTest1_MN AI Agents Yes 1 >7 years SkillCategoryTest1_MN Digital: Azure Machine Learning (ML) Yes 1 >7 years SkillCategoryTest1_MN Digital: Deep Learning Yes 1 >7 years SkillCategoryTest1_MN Digital: DevOps Continuous Integration and Continuous Delivery (CI/CD) Yes 1 >7 years SkillCategoryTest1_MN Digital: Microservices Yes 1 >7 years SkillCategoryTest1_MN Digital: ReactJS Yes 1 >7 years SkillCategoryTest1_MN Digital: Spring Boot Yes 1 >7 years SkillCategoryTest1_MN Generative AI Yes 1 >7 years Skills: Digital: Deep Learning~Digital: DevOps Continuous Integration and Continuous Delivery (CI/CD)~Digital: ReactJS~Digital: Microservices~Digital: Spring Boot~Digital: Azure Machine Learning (ML)~Generative AI~AI Agents.

Pay Rate Range: $ 53.03 - 56.81/hr.

Role Name: AI/ML & Forward Deployed Engineer (8+ Years)

Job DescriptionL
We are looking for an experienced AI/ML & Forward Deployed Engineer with 8+ years of engineering experience to deliver high-impact AI/ML (and GenAI, where applicable) solutions end-to-end. You will blend applied machine learning, software engineering, and stakeholder problem-solving to deploy production-grade systems that are scalable, secure, observable, and aligned to business KPIs.

This role is ideal for engineers who enjoy operating at the intersection of data + models + systems + real users, and who can thrive in ambiguous, fast-moving environments

Key Responsibilities

1) Use-Case Discovery & Forward Deployment
· Partner with stakeholders (business/product/customers) to identify and shape AI opportunities into well-defined use cases with success metrics, constraints, and rollout plans.
· Run workshops and technical discovery to assess feasibility, data readiness, integration needs, and operational risks.
· Drive rapid prototyping, pilot deployments, and iterative improvements based on real user feedback.

2) Applied ML Engineering (Classic ML + Deep Learning)
· Develop and improve ML solutions (classification, regression, ranking, forecasting, anomaly detection, NLP).
· Establish and maintain robust evaluation practices: offline metrics, validation strategies, experimentation, and A/B testing.
· Perform feature engineering, error analysis, model optimization, and performance tuning for production requirements.

3) GenAI / LLM Engineering (If Applicable)
· Build and productionize RAG (Retrieval-Augmented Generation) pipelines, including document ingestion, chunking strategy, embeddings, retrieval tuning, reranking, and response grounding.
· Implement guardrails and reliability patterns: prompt templates, tool/function calling, hallucination reduction, citation strategies, and fallback paths.
· Develop evaluation harnesses for GenAI: quality metrics, regression tests, safety tests, and human-in-the-loop workflows.

4) Productionization (MLOps / LLMOps)
· Package models into scalable services and deploy using Docker/Kubernetes and CI/CD.
· Implement model lifecycle management: model registry, versioning, automated retraining triggers, and governance workflows.
· Build monitoring and observability: drift detection, latency/throughput monitoring, error tracking, alerting, and rollback mechanisms.

5) Systems Integration & Platform Collaboration
· Build integration layers (REST/gRPC APIs, event-driven services) to embed AI capabilities into products and enterprise workflows.
· Collaborate with data engineers to design reliable pipelines and ensure data quality, lineage, and governance.
· Ensure secure and compliant design (PII/PHI handling, RBAC, secrets management, encryption, audit trails).

6) Technical Leadership & Enablement
· Provide technical guidance and mentoring to engineers; lead design reviews and establish best practices.
· Document solutions with architecture diagrams, runbooks, and operational playbooks.
· Create reusable accelerators (templates, libraries, patterns) to scale deployments across teams or customers.

Required Qualifications

· Programming & Scripting
o Languages:
§ UI Skills using React JS (Primary) If not the Angular
§ Python (primary for automation, APIs, data pipelines)

· API & Backend Engineering
o REST API development (Spring Boot / FastAPI / Node.js)
o API integration using:
§ OAuth2 / JWT authentication
§ API gateways (Azure API Management, Apigee)
o Data exchange formats: JSON, XML
§ HL7/FHIR (important in healthcare) – Secondary or nice to have

· AI/ML & GenAI Integration
o LLM integration:
§ Azure OpenAI / OpenAI APIs
o Frameworks: LangChain, Semantic Kernel
o RAG (Retrieval-Augmented Generation)
o Prompt engineering
o Embeddings + vector DBs (Pinecone, Azure Cognitive Search)

· Cloud & Infrastructure
o Azure (preferred in Optum ecosystem):
§ Azure App Services
§ Azure Functions (serverless)
§ Azure Kubernetes Service (AKS)
§ Azure Storage / Blob / Cosmos DB
o AWS (secondary):
§ Lambda, ECS/EKS, S3

· Data Engineering & Handling
o Any SQL RDBMS
o NoSQL - MongoDB preferred if not Cosmos DB

Preferred Qualifications (Nice to Have)
· Forward-deployed / customer-embedded delivery experience (consulting, solutions engineering, implementation engineering).
· Infrastructure as Code (IaC)- Terraform / ARM templates / Bicep (Nice to have
· Experience with vector databases and search: Azure AI Search, Elasticsearch/OpenSearch, Pinecone, Weaviate, Milvus.
· Experience with platforms/tools: Databricks/Spark, MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI.
· Experience with Responsible AI: model governance, fairness testing, explainability, audit readiness.
· Domain expertise (optional): healthcare, PBM

Core Skills (What You'll Use Often)
· Software development: Programming language and database skills
· ML: training, evaluation, feature engineering, error analysis, model serving
· GenAI (optional): RAG, retrieval tuning, prompt orchestration, guardrails, evaluations
· Software Engineering: APIs/microservices, integration, performance optimization
· MLOps/LLMOps: CI/CD, monitoring, drift, versioning, rollout/rollback
· Cloud & Platform: compute/storage/IAM/networking, containers, Kubernetes
· Security: secrets, RBAC, encryption, compliance-aware design

Success Metrics (How We Measure Impac


Essential Skills: An experienced AIML Forward Deployed Engineer with 8 years of engineering experience to deliver high-impact AIML (and GenAI| where applicable) solutions end-to-end. You will blend applied machine learning| software engineering| and stakeholder problem-solving to deploy production-grade systems that are scalable| secure| observable| and aligned to business KPIs.This role is ideal for engineers who enjoy operating at the intersection of data models systems real users| and who can thrive in ambiguous| fast-moving environments

Skills: Digital : Deep Learning~Digital : DevOps Continuous Integration and Continuous Delivery (CI/CD)~Digital : ReactJS~Digital : Microservices~Digital : Spring Boot~Digital : Azure Machine Learning (ML)~Generative AI~AI Agents

Experience Required: 6-8 years

 

Skills:

Category Name Required Importance Experience SkillCategoryTest1_MN AI Agents Yes 1 >7 years   SkillCategoryTest1_MN Digital : Azure Machine Learning (ML) Yes 1 >7 years   SkillCategoryTest1_MN Digital : Deep Learning Yes 1 >7 years   SkillCategoryTest1_MN Digital : DevOps Continuous Integration and Continuous Delivery (CI/CD) Yes 1 >7 years   SkillCategoryTest1_MN Digital : Microservices Yes 1 >7 years   SkillCategoryTest1_MN Digital : ReactJS Yes 1 >7 years   SkillCategoryTest1_MN Digital : Spring Boot Yes 1 >7 years   SkillCategoryTest1_MN Generative AI Yes 1 >7 years