• Support the design, build and deployment of applied AI systems under the guidance of senior engineers. • Contribute to backend services, APIs and application components that integrate AI capabilities into production systems. • Assist in experimentation, prototyping and testing of ML and Generative AI solutions. • Write clean, maintainable, well-tested code following established engineering standards. • Participate in code reviews and technical discussions to continuously improve engineering quality. • Help implement monitoring, evaluation and performance tracking for AI-enabled systems. • Collaborate with data scientists, product managers and client stakeholders to understand requirements and translate them into working solutions. • Contribute to internal knowledge sharing and capability development as you grow.

Description About Company JobHire.ai is a vertical AI agent for automated job search. We help thousands of job seekers land interviews faster by finding, tailoring, and applying to jobs on their behalf. We’re profitable, growing fast, and now expanding the product to deliver long-term user value beyond job offers alone. 📈 ~35% MoM; top 1% in growth rate 💰 Profitable from day one 👥 40 people 🚀 Investors: Deel Ventures, Daniel Gutenberg, Dave Waiser, Margulan Seisembayev, and other unicorn founders. Mission  JobHire.ai is a personal AI agent for continuous professional development and happiness at work The role Overview We are looking for an AI Engineering Tech Lead to architect, build, and lead our core AI products. You will split your time between AI development (60%) and technical leadership & mentoring. You will own the technical roadmap for AI features, from candidate matching to resume parsing and conversational agents. Key Responsibilities Technical Leadership - Define best practices for model development, MLOps, code reviews, and testing. - Collaborate with product managers to translate business goals (e.g., match quality, latency) into technical specifications. - Drive architectural decisions for AI services (inference pipelines, embedding stores, LLM orchestration). - Conduct regular design reviews and ensure technical documentation. AI Engineering - Design and fine-tune transformer-based models for resume/job description understanding. - Build and optimize vector search pipelines for semantic candidate matching. - Develop agentic workflows using LLMs (GPT-4, Claude, Gemini) for screening, interview scheduling, and feedback synthesis. - Implement production RAG systems with low latency. - Build data pipelines for feature extraction, embedding generation, and model evaluation. - Monitor model performance, drift, and retraining triggers. Cross-functional Collaboration - Work closely with backend engineers to deploy models via REST APIs (FastAPI, TorchServe, or similar). - Partner with data engineers to ensure high-quality training data from hiring interactions. - Coordinate with product & design to embed AI features seamlessly into UX. - Ensure compliance with data privacy (GDPR, CCPA) and fairness/anti-bias standards.

Description About Company JobHire.ai is a vertical AI agent for automated job search. We help thousands of job seekers land interviews faster by finding, tailoring, and applying to jobs on their behalf. We’re profitable, growing fast, and now expanding the product to deliver long-term user value beyond job offers alone. 📈 ~35% MoM; top 1% in growth rate 💰 Profitable from day one 👥 40 people 🚀 Investors: Deel Ventures, Daniel Gutenberg, Dave Waiser, Margulan Seisembayev, and other unicorn founders. Mission  JobHire.ai is a personal AI agent for continuous professional development and happiness at work The role Overview We are looking for an AI Engineering Tech Lead to architect, build, and lead our core AI products. You will split your time between AI development (60%) and technical leadership & mentoring. You will own the technical roadmap for AI features, from candidate matching to resume parsing and conversational agents. Key Responsibilities Technical Leadership - Define best practices for model development, MLOps, code reviews, and testing. - Collaborate with product managers to translate business goals (e.g., match quality, latency) into technical specifications. - Drive architectural decisions for AI services (inference pipelines, embedding stores, LLM orchestration). - Conduct regular design reviews and ensure technical documentation. AI Engineering - Design and fine-tune transformer-based models for resume/job description understanding. - Build and optimize vector search pipelines for semantic candidate matching. - Develop agentic workflows using LLMs (GPT-4, Claude, Gemini) for screening, interview scheduling, and feedback synthesis. - Implement production RAG systems with low latency. - Build data pipelines for feature extraction, embedding generation, and model evaluation. - Monitor model performance, drift, and retraining triggers. Cross-functional Collaboration - Work closely with backend engineers to deploy models via REST APIs (FastAPI, TorchServe, or similar). - Partner with data engineers to ensure high-quality training data from hiring interactions. - Coordinate with product & design to embed AI features seamlessly into UX. - Ensure compliance with data privacy (GDPR, CCPA) and fairness/anti-bias standards.

Role Description This is a high-stakes, execution-focused role within the Transformation Office. We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives. Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services. You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud environment. Key Responsibilities - Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio). - LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. - Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows. - Automated Model Evaluation: Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment. - Observability & Monitoring: Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost. - Infrastructure as Code (IaC): Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate. - Advanced Analytics Integration: Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models. - IT & Security Diplomacy: Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation. - Scalable Inference Engineering: Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate. - Prompt & Model Versioning: Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability. - Data Science Engineering: Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices. - Security & Compliance Hardening: Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage. Qualifications - Education: Bachelor’s degree in computer science or a related field required; master’s degree in a quantitative discipline highly desirable. - Proven Execution: 6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment. - AWS & Azure Mastery: Deep, hands-on proficiency in both ecosystems. You must be able to configure Bedrock and Azure OpenAI services, including private networking and endpoint security, on day one. - Technical Stack: Expert Python, SQL, and PySpark. Extensive experience with containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, or Step Functions). - LLM Tooling: Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs. - Data Science Flavor: A strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance. - Transformation Mindset: The ability to move at the speed of a startup while maintaining the collaborative relationships required to function within a large-scale enterprise IT landscape. Working Conditions - May on occasion be exposed to loud sounds and distracting noise levels, such as from office equipment. - The ability to lift up to 30lbs. - Use of computers and technology. Benefits - Health care and 401(k) savings plans.