Job description

AI Engineer Location: Austin Texas. Type: Fixed Term Employee (6 months with possible extension, including Benefits). Job Summary The AI Engineer will play a key role in driving data-driven decision-making and intelligent automation within our organization. You will be responsible for the architectural design, optimization, and deployment of Agentic AI solutions and Large Language Models (LLMs/SLMs). Focusing on building robust pipelines, optimizing model performance for specific hardware constraints, and developing sophisticated agent orchestration frameworks, you will solve complex business automation challenges Responsibilities AI System Architecture & Implementation: • Model Deployment & Optimization: Lead the end-to-end integration of machine learning models and fine-tuned SLMs into production environments, focusing on model compression, latency reduction, and hardware-specific optimization. • Agentic Workflows: Design and implement autonomous agent architectures, including multi-step reasoning engines, tool-use integration, and structured decision-making frameworks. • Efficient Fine-Tuning Implementation: Develop and maintain the infrastructure for Parameter-Efficient Fine-Tuning (PEFT), implementing techniques like LoRA, QLoRA, or Adapter-tuning to minimize computational overhead. • Retrieval Augmented Generation (RAG): Build and maintain high-performance vector databases and semantic search indices to enable context-aware AI responses and sub-second data retrieval. Data Engineering & Pipeline Development: • Automated Data Pipelines: Develop scalable, automated pipelines for the cleaning, normalization, and feature engineering of high-velocity raw data streams. • Quality Assurance: Collaborate with Data Scientists to establish "Ground Truth" datasets and implement automated validation layers to ensure model output reliability and safety. • System Monitoring: Design and implement monitoring solutions to track model drift, inference performance, and resource utilization in production. Technical Leadership & Integration: • Cross-Functional Collaboration: Work closely with Data Science, Data Engineering, and DevOps teams to ensure seamless transition from model prototype to hardened production binary. • Mentorship: Provide technical guidance and code reviews for junior engineers, championing best practices in software engineering and AI deployment. • Stakeholder Engagement & Consulting: Translate complex technical constraints (e.g., memory limits, inference speed) into clear trade-offs for client stakeholders and project leadership. Eligibility Requirements • Experience: Minimum of 5+ years of experience in Software Engineering or Machine Learning Engineering, with a proven track record of deploying AI models in production. • Technical Stack (Expert Level): • Languages: Expert proficiency in Python; familiarity with lower-level languages (C++/Rust) or Go for performance-critical components is preferred. • AI Frameworks: Deep experience with PyTorch, TensorFlow, or JAX, and libraries for model adaptation and inference (e.g., Hugging Face ecosystem). • Data Infrastructure: Hands-on experience with SQL/NoSQL databases, Vector Databases, and cloud-native AI services (AWS, GCP, or Azure). • Engineering Rigor: Demonstrated mastery of version control (Git), CI/CD pipelines, containerization (Docker/Kubernetes), and API design (REST/gRPC). • Problem Solving: Proven ability to optimize models for restricted resource environments (memory, CPU/GPU limits) without compromising core performance. • PEFT & Adaptability: Deep experience with PEFT libraries (e.g., Hugging Face PEFT) and fine-tuning frameworks Preferred Qulification • Experience working in a cross-functional and client-facing team environment within an outsourcing or consulting framework. • Familiarity with workflow automation and enterprise analytics tools (e.g., Alteryx, advanced BI pipelines). Core Competencies • Technical depth with client-facing communication • Systems thinking and compatibility validation • Precision and process discipline In the US, the target base salary for this role is $175,000-$180,000. Compensation is based on a range of factors that include relevant experience, knowledge, skills, other job-related qualifications, and geography. We expect the majority of candidates who are offered roles at our company to fall throughout the range based on these factors About eClerx eClerx is a leading provider of productized services, bringing together people, technology and domain expertise to amplify business results. The firm provides business process management, automation, and analytics services to a number of Fortune 2000 enterprises, including some of the world’s leading financial services, communications, retail, fashion, media & entertainment, manufacturing, travel & leisure, and technology companies. Incorporated in 2000, eClerx is traded on both the Bombay and National Stock Exchanges of India. The firm employs more than 19,000 people across Australia, Canada, France, Germany, Switzerland, Egypt. India, Italy, Netherlands, Peru, Philippines, Singapore, Thailand, the UK, and the USA. For more information, visit www.eclerx.com You can also find us on: linkedin.com indeed.com glassdoor.com eClerx is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law. We are also committed to protecting and safeguarding your personal data. Please find our policy here

Requirements

Must-haves

Location: Austin Texas.
Type: Fixed Term Employee (6 months with possible extension, including Benefits).
Agentic Workflows: Design and implement autonomous agent architectures, including multi-step reasoning engines, tool-use integration, and structured decision-making frameworks.
Efficient Fine-Tuning Implementation: Develop and maintain the infrastructure for Parameter-Efficient Fine-Tuning (PEFT), implementing techniques like LoRA, QLoRA, or Adapter-tuning to minimize computational overhead.
Retrieval Augmented Generation (RAG): Build and maintain high-performance vector databases and semantic search indices to enable context-aware AI responses and sub-second data retrieval.
Automated Data Pipelines: Develop scalable, automated pipelines for the cleaning, normalization, and feature engineering of high-velocity raw data streams.
Quality Assurance: Collaborate with Data Scientists to establish "Ground Truth" datasets and implement automated validation layers to ensure model output reliability and safety.
System Monitoring: Design and implement monitoring solutions to track model drift, inference performance, and resource utilization in production.
Stakeholder Engagement & Consulting: Translate complex technical constraints (e.g., memory limits, inference speed) into clear trade-offs for client stakeholders and project leadership.
Eligibility Requirements
Experience: Minimum of 5+ years of experience in Software Engineering or Machine Learning Engineering, with a proven track record of deploying AI models in production.
AI Frameworks: Deep experience with PyTorch, TensorFlow, or JAX, and libraries for model adaptation and inference (e.g., Hugging Face ecosystem).
Data Infrastructure: Hands-on experience with SQL/NoSQL databases, Vector Databases, and cloud-native AI services (AWS, GCP, or Azure).
Engineering Rigor: Demonstrated mastery of version control (Git), CI/CD pipelines, containerization (Docker/Kubernetes), and API design (REST/gRPC).
Problem Solving: Proven ability to optimize models for restricted resource environments (memory, CPU/GPU limits) without compromising core performance.
PEFT & Adaptability: Deep experience with PEFT libraries (e.g., Hugging Face PEFT) and fine-tuning frameworks
Experience working in a cross-functional and client-facing team environment within an outsourcing or consulting framework.
Familiarity with workflow automation and enterprise analytics tools (e.g., Alteryx, advanced BI pipelines).
Core Competencies
Technical depth with client-facing communication
Systems thinking and compatibility validation
Precision and process discipline

Nice-to-haves

Technical Stack (Expert Level): • Languages: Expert proficiency in Python; familiarity with lower-level languages (C++/Rust) or Go for performance-critical components is preferred.
Preferred Qulification

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonrustgosqlawsgcpazureci/cddockerkubernetesapirest

Platform: oracle-cloud
Effort: medium