Experience: 5–10+ Years
Role Focus: Generative AI, AI Architecture, Applied Machine Learning, Speech & Audio AI, and Real-Time AI Systems

Position Overview

We are seeking a highly skilled and hands-on AI Architect / Applied AI Scientist to design, develop, optimize, and deploy scalable, production-ready AI solutions across enterprise, cloud, and edge environments.

The ideal candidate will combine strong expertise in Generative AI, Large Language Models (LLMs), machine learning, natural language processing, and speech and audio processing with a proven ability to translate advanced AI research into practical, high-performance applications.

In this role, you will architect and build intelligent AI solutions encompassing Retrieval-Augmented Generation (RAG), agentic AI, document intelligence, speech recognition, audio enhancement, and real-time AI processing. You will work across the entire AI development lifecycle—from research, experimentation, and solution architecture to hands-on implementation, model optimization, enterprise integration, and production deployment.

The successful candidate will deliver reliable, secure, and scalable AI solutions that address real-world business challenges. You will develop intelligent applications that improve automation, accuracy, and operational efficiency while optimizing model performance, inference latency, infrastructure costs, and resource utilization. You will also bridge AI research and software engineering by evaluating emerging technologies, developing proof-of-concepts, and transforming promising innovations into production-ready capabilities.

This role requires a strong combination of AI expertise, software engineering skills, architectural thinking, and practical problem-solving. The ideal candidate will be comfortable working independently and collaborating with cross-functional engineering and product teams to deliver measurable business value through innovative AI solutions.

Key Responsibilities

1. Generative AI & LLM Solutions

• Design, develop, and deploy enterprise-grade AI applications using LLMs and Generative AI technologies.
• Build and optimize RAG pipelines, semantic search solutions, and context-aware AI applications.
• Design and implement agentic AI workflows using frameworks such as LangGraph, LangChain, and Semantic Kernel.
• Develop prompt engineering strategies, structured output generation, tool calling, and multi-step reasoning workflows.
• Evaluate and apply model fine-tuning, adaptation, and optimization techniques to improve accuracy, performance, and cost efficiency.

2. Applied AI & Document Intelligence

• Develop AI-powered solutions for document processing, information extraction, classification, summarization, and validation.
• Build intelligent pipelines for processing structured, semi-structured, and unstructured data.
• Apply NLP, deep learning, and transformer-based models to solve complex business problems.
• Establish model evaluation frameworks and quality metrics to measure accuracy, reliability, and business effectiveness.

3. Speech, Audio & Real-Time AI Processing

• Design and develop AI-powered speech and audio processing solutions, including speech recognition, transcription, audio enhancement, and noise suppression.
• Optimize speech models for diverse acoustic environments, background noise, communication channels, and domain-specific terminology.
• Develop low-latency, real-time audio processing pipelines suitable for interactive and communication-intensive applications.
• Integrate speech AI with LLMs and other AI components to enable intelligent voice-driven applications and workflows.
• Evaluate and optimize end-to-end system performance, including latency, throughput, recognition accuracy, and resource utilization.

4. AI Architecture & Enterprise Integration

• Architect scalable, modular, and secure AI solutions aligned with enterprise architecture and business requirements.
• Integrate AI capabilities with enterprise applications, RESTful APIs, microservices, and cloud infrastructure.
• Design robust AI service architectures with appropriate observability, error handling, security controls, and scalability.
• Collaborate with software engineering, platform, and product teams to integrate AI capabilities into existing enterprise ecosystems.
• Establish best practices for AI application development, deployment, versioning, monitoring, and lifecycle management.

5. Model Optimization, Edge AI & Deployment

• Optimize AI/ML models for inference performance, memory consumption, throughput, and deployment efficiency.
• Deploy and run AI/ML models in cloud environments, CPU-based systems, edge devices, and resource-constrained computing platforms.
• Evaluate model compression, quantization, and other optimization techniques where appropriate.
• Design deployment strategies that balance model accuracy, latency, infrastructure cost, and operational constraints.
• Implement practices for monitoring model performance, identifying degradation, and maintaining production reliability.

6. Research, Innovation & Technical Leadership

• Research emerging AI technologies, models, frameworks, and deployment techniques.
• Lead technical evaluations, proof-of-concepts (POCs), and feasibility studies, taking promising solutions through production deployment.
• Translate research findings into practical AI capabilities that meet performance, scalability, and business requirements.
• Collaborate with cross-functional teams to identify AI opportunities and deliver measurable business value.
• Contribute to technical architecture decisions, engineering standards, and best practices for enterprise AI development.

Required Technical Skills

Programming & Software Engineering

• Strong Python programming skills, including experience building production-quality AI/ML applications.
• Solid understanding of software engineering principles, API development, and application integration.

Artificial Intelligence & Machine Learning

• Strong knowledge of machine learning, deep learning, NLP, transformer architectures, and LLM-based applications.
• Hands-on experience with Generative AI, RAG, embeddings, semantic search, and vector databases.
• Experience with model evaluation, prompt engineering, fine-tuning, and inference optimization.
• Familiarity with speech recognition, audio processing, or other real-time AI applications is highly desirable.

LLM Platforms & AI Frameworks

• Experience working with one or more major LLM platforms, such as OpenAI, Azure OpenAI, Google Gemini, or Anthropic Claude.
• Hands-on experience with AI development frameworks such as LangChain, LangGraph, Semantic Kernel, or Hugging Face.
• Understanding of agentic AI patterns, tool calling, orchestration, and multi-step AI workflows.

Cloud & Deployment Technologies

• Experience deploying AI/ML applications on AWS and/or Microsoft Azure.
• Working knowledge of Docker, Kubernetes, and containerized application deployment.
• Understanding of scalable cloud architectures, distributed processing, and production deployment practices.

AI Engineering & Operations

• Knowledge of LLMOps/MLOps, model serving, monitoring, evaluation, and lifecycle management.
• Experience with vector databases, retrieval optimization, and AI application performance tuning.
• Understanding of security, scalability, reliability, and cost optimization considerations for production AI systems.

Preferred Skills

• Working knowledge of C#, .NET, and ASP.NET Core.
• Experience integrating Python-based AI/ML services with .NET enterprise applications.
• Experience developing RESTful APIs and AI-enabled microservices.
• Familiarity with cloud-native architecture, distributed systems, and enterprise application integration patterns.
• Experience with speech-to-text, audio enhancement, noise suppression, voice activity detection, or real-time communication systems.
• Experience deploying and optimizing AI models for CPU-only environments, edge devices, or other resource-constrained platforms.
• Familiarity with model quantization, ONNX Runtime, OpenVINO, or similar inference optimization technologies.

Education & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
• At least 5 years of relevant software engineering, AI/ML engineering, or applied AI experience.
• Demonstrated experience designing, developing, and deploying AI/ML or Generative AI solutions in production or production-oriented environments.
• Strong analytical, problem-solving, and troubleshooting skills.
• Ability to communicate complex technical concepts effectively to technical and non-technical stakeholders.
• U.S. citizenship required.