Sr AI Platform Engineer x2

MatchPoint Solutions is a fast-growing, young, energetic global IT-Engineering services company with clients across the US . We provide technology solutions to various clients like Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more! More recently, we have expanded to working internationally in Canada, China, Ireland, UK, Brazil, and India . Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.

We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!

Job Title: AI Platform Engineer
Location : Remote
Duration : 12 Months
Rate $80 to $85 per hour

Job Overview

We are seeking an experienced **AI Platform Engineer** to design, build, deploy, and operate enterprise-scale Artificial Intelligence (AI) and Generative AI platforms.
The ideal candidate will have strong experience in software engineering, cloud infrastructure, AI/ML platforms, and MLOps/LLMOps. The role will focus on building secure, scalable, and reliable platforms that enable development teams, data scientists, and business teams to develop and operate Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and agentic AI applications.

The successful candidate will work closely with AI/ML engineers, software engineers, data scientists, architects, DevOps teams, cybersecurity teams, and business stakeholders to establish and maintain enterprise AI platform capabilities.

## Key Responsibilities

### 1. AI Platform Architecture
- Design and implement scalable, secure, and highly available enterprise AI platforms.
- Develop reusable architectures and standardized deployment patterns for AI/ML workloads.
- Build infrastructure supporting LLMs, RAG, agentic AI, and traditional machine learning applications.
- Design APIs and abstraction layers to support multiple AI models and providers.
- Evaluate AI frameworks, cloud services, infrastructure technologies, and model providers.
- Establish standards for AI application development, deployment, monitoring, and governance.

### 2. Generative AI and LLM Platform Engineering
- Build and maintain enterprise infrastructure for LLM-powered applications.
- Integrate commercial and open-source models, including OpenAI, Azure OpenAI, Anthropic, Google Gemini, and open-source alternatives.
- Develop and maintain LLM gateways, model-routing services, and reusable AI APIs or SDKs.
- Implement model routing, fallback mechanisms, rate limiting, throttling, and usage controls.
- Support prompt management, model configuration, versioning, function calling, and tool calling.
- Track token consumption, model usage, latency, and AI application costs.
- Support conversation management, session handling, context management, and application state.

### 3. RAG Platform Engineering
- Build and maintain reusable enterprise RAG platform capabilities.
- Develop document ingestion and processing pipelines, including parsing, chunking, metadata extraction, and embedding generation.
- Implement indexing, retrieval, reranking, and semantic, keyword, and hybrid search.
- Deploy and manage vector databases and enterprise search technologies.
- Work with platforms such as Azure AI Search, OpenSearch, Elasticsearch, Pinecone, Weaviate, Milvus, or equivalent technologies.
- Develop APIs for accessing enterprise knowledge repositories.
- Implement source attribution, citations, metadata filtering, and permission-aware retrieval.
- Optimize retrieval accuracy, performance, scalability, and cost.
- Automate the ingestion and synchronization of enterprise knowledge sources.

### 4. Agentic AI Platform Engineering
- Build platform capabilities for AI agents and agentic workflows.
- Work with frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, or equivalent technologies.
- Develop infrastructure for tool calling, function calling, and enterprise system integration.
- Support multi-step AI workflows involving planning, retrieval, tool execution, and validation.
- Implement secure controls for agent permissions, tool access, execution limits, and human approvals.
- Support workflow orchestration, state management, memory, and agent observability.
- Develop reusable agent services and infrastructure for AI application teams.

### 5. Cloud and Infrastructure Engineering
- Design and deploy AI platforms on AWS, Microsoft Azure, Google Cloud, or hybrid environments.
- Build cloud-native infrastructure using Kubernetes, Docker, and container orchestration technologies.
- Implement Infrastructure as Code (IaC) using Terraform, CloudFormation, Bicep, or equivalent tools.
- Design and manage compute, networking, storage, identity, and security components.
- Implement autoscaling and resource management for AI workloads.
- Optimize infrastructure for performance, reliability, availability, and cost.
- Support GPU-based infrastructure and workloads where applicable.
- Establish standardized development, testing, staging, and production environments.

### 6. MLOps and LLMOps
- Build CI/CD pipelines for AI/ML application development and deployment.
- Implement model lifecycle management, versioning, and release processes.
- Manage versioning for models, prompts, configurations, datasets, and evaluation criteria.
- Automate testing and validation before production deployment.
- Develop evaluation pipelines for LLM and RAG applications.
- Monitor model performance, application quality, latency, token consumption, and cost.
- Implement rollback procedures and controlled production releases.
- Establish repeatable processes for AI experimentation and productionization.
- Collaborate with data science and AI engineering teams to deploy models and applications reliably.

### 7. AI Observability and Reliability
- Implement end-to-end monitoring and observability across AI platforms and applications.
- Monitor API performance, model latency, throughput, token usage, errors, and availability.
- Implement distributed tracing across LLM, RAG, agent, API, and data components.
- Monitor retrieval quality and AI application performance.
- Develop dashboards, alerts, operational metrics, and service-level objectives (SLOs).
- Troubleshoot production issues across infrastructure, APIs, models, databases, and AI workflows.
- Participate in incident response, root-cause analysis, and continuous improvement.

### 8. AI Security, Governance, and Responsible AI
- Design secure architectures for enterprise AI platforms and applications.
- Implement authentication, authorization, role-based access control (RBAC), identity management, and access policies.
- Ensure AI applications respect enterprise data permissions and security requirements.
- Implement protections against prompt injection, data leakage, unauthorized tool execution, and insecure AI integrations.
- Protect sensitive information throughout LLM and RAG workflows.
- Implement encryption, secure secrets management, and appropriate data protection controls.
- Establish guardrails and policy enforcement mechanisms for AI applications.
- Support AI governance, risk management, auditability, and compliance requirements.
- Define governance controls for models, prompts, data, and AI applications.
- Collaborate with cybersecurity and compliance teams to maintain enterprise AI security standards.

Technical Environment

The role may involve the following technologies and platforms:

Programming and APIs:** Python, REST APIs, SDKs
AI and Generative AI:** LLMs, RAG, embeddings, prompt engineering, agentic AI
-AI Frameworks:** LangChain, LangGraph, LlamaIndex, Semantic Kernel
Cloud Platforms:** AWS, Microsoft Azure, Google Cloud
Containers and Orchestration:** Docker, Kubernetes
Infrastructure as Code:** Terraform, CloudFormation, Bicep
-Vector Databases and Search:** Azure AI Search, OpenSearch, Elasticsearch, Pinecone, Weaviate, Milvus
MLOps/LLMOps:** CI/CD, model lifecycle management, automated evaluation, deployment automation
Observability and Security:** Distributed tracing, monitoring, RBAC, secrets management, AI guardrails

MatchPoint Solutions 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.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.