Key Facts

Remote From:

Full time

English

Hard Skills

Prompt Engineering Large Language Modeling Python (Programming Language) Personal Security Observability Amazon MQ Application Programming Interface (API) AI/ML Inference Cloud-Native Applications Reliability Engineering AWS Cloud Services Scalability AWS Bedrock Feature Engineering Microsoft Azure Debugging Azure Functions Azure OpenAI AWS Lambda Embedding Microservices Performance Improvement Ethical AI CI/CD Data Collection Amazon API Gateway Opensearch Azure AI Studio Azure Cognitive Services Azure Machine Learning Data Preprocessing Data Transformation AWS SageMaker MLOps (Machine Learning Operations) Integration Platforms Compliance Management Amazon S3 Continuous Monitoring Deployment Project Semantic Analysis Vector Data FastAPI Flask (Web Framework) Document Classification Sales Management Pair Programming Code Analysis Business Analysis Demonstration Skills Client Services Stakeholder Engagement Critical Design Review (CDR)

Other Skills

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Team Leadership
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Collaboration
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Communication
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Mentorship

Roles & Responsibilities

Strong hands-on Python development experience (non-negotiable)
Proven experience building and deploying AI/ML/GenAI solutions in production
Experience with LLMs, prompt engineering, RAG, agents, embeddings
REST APIs, SDK-based integrations, microservices; cloud-native development on AWS and/or Azure

Requirements:

Design, develop, and deploy AI/GenAI solutions in Python, including data ingestion/preprocessing, RAG pipelines, prompt engineering, and API/microservice components
Architect cloud-native AI architectures on AWS and/or Azure, integrating with enterprise data platforms and ensuring security/compliance
Own end-to-end AI delivery: POCs, production deployments, code/design reviews, and implementing MLOps/LLMOps with CI/CD, monitoring, and observability
Engage with clients and COE teams: translate requirements, demonstrate prototypes, mentor engineers, and support pre-sales with credible implementation plans

Job description

Key Responsibilities
Hands‐On AI Solution Development (Core Requirement)

Design, develop, and implement AI / GenAI solutions using Python as the primary programming language
Build production‐ready AI components, including:
- Data ingestion and preprocessing pipelines
- Prompt engineering and prompt orchestration layers
- Retrieval‐Augmented Generation (RAG) pipelines
- API‐based AI services and microservices
Write and review Python code for:
- Model integration and inference
- LLM orchestration (agents, tools, workflows)
- Data transformations and feature engineering
Debug, optimize, and harden AI solutions for performance, scalability, and reliability

Cloud & Hyperscaler AI Implementation

Implement AI solutions using:
- AWS (Bedrock, SageMaker, Lambda, API Gateway, Amazon Q, OpenSearch, S3, etc.)
- Azure (Azure OpenAI, Azure AI Studio, Cognitive Services, Azure ML, Functions, etc.)
Develop cloud‐native AI architectures leveraging Python‐based SDKs and APIs
Integrate AI services with enterprise data platforms, applications, and security frameworks

Architecture & Delivery Leadership (Hands‐On)

Own end‐to‐end AI solution architecture, from design through deployment
Actively participate in:
- POCs and pilots (hands‐on build)
- Production deployments (implementation support)
- Code reviews and design reviews
Define and implement MLOps / LLMOps patterns, including CI/CD, monitoring, and observability
Ensure solutions meet Responsible AI, security, and compliance standards

Client & Stakeholder Engagement

Work directly with client technical and business stakeholders to:
- Translate business requirements into working AI solutions
- Demonstrate prototypes and explain implementation decisions
Serve as the technical AI face for clients—credible because of hands‐on depth, not just strategy

COE & Team Enablement

Mentor onshore and offshore engineers through pair programming, code reviews, and design sessions
Define reusable Python frameworks, accelerators, and templates for AI delivery
Contribute to AI COE standards, reference architectures, and best practices

Pre‐Sales & Growth Support

Support pre‐sales by:
- Building working demos and POCs (not just slides)
- Reviewing technical feasibility and effort estimates
Contribute to proposals with implementation‐level credibility

Required Qualifications

Strong hands‐on Python development experience (non‐negotiable)
Proven experience building and deploying AI / ML / GenAI solutions, not just designing them
Experience with:
- LLMs, prompt engineering, RAG, agents, embeddings
- REST APIs, SDK‐based integrations, microservices
- Cloud‐native development on AWS and/or Azure

Experience Profile

10–15+ years overall experience with significant recent hands‐on AI work
5+ years delivering AI / ML / Advanced Analytics solutions in production
Experience working in client‐facing, onshore roles

Preferred (but not mandatory)

Experience with FastAPI / Flask for Python‐based AI services
Familiarity with vector databases, search, or document intelligence
Hyperscaler AI certifications (AWS / Azure)

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.

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