Machine Learning Engineer at Tyndale

Bedminster Township, Pennsylvania, United States -

Full Time

Start Date

Immediate

Expiry Date

05 Jun, 26

Salary

0.0

Posted On

07 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML Lifecycle, Generative AI, POCs, Automated Tests, Containerisation, AWS Pipeline, RAG, LLM, Vector Stores, LangChain, LlamaIndex, Inference APIs, FastAPI, AWS Lambda, Terraform, PySpark

Industry

Apparel & Fashion

Description

Overview The Tyndale Company is seeking a Machine Learning Engineer to join our growing AI organization. This role will play a critical part in our mission to harness data for advanced AI and Machine Learning applications. You will collaborate with data scientists, data engineers and business stakeholders to own the full lifecycle of our ML models and Generative AI applications. This is an exciting opportunity to shape the foundation of our AI and Machine Learning efforts, driving impactful insights and innovations. HYBRID/REMOTE: Tyndale supports a strong work-life balance. This opportunity requires onsite work a minimum of 1 day per week, and 4 days per week remotely. To be considered, candidates must reside within a commutable distance from our corporate headquarters in Pipersville, PA (Bucks County) or our location in Houston, TX (City Centre). The Tyndale Company is a private, 9x Top Workplace winner in PA and 5x winner in TX, and an industry leading national supplier of arc-rated flame-resistant clothing (FRC) to the energy sector – including utilities, oil and gas, transportation, chemical manufacturing, and NFPA 70E markets. We’re a family-owned business providing a retail-style apparel experience to hundreds of thousands of energy workers across the US and Canada. We’re the leading distributor of innovative FRC solutions, and the largest industrial supplier of Carhartt FR, Ariat FR, and Wrangler FR clothing. Responsibilities Own the Full Lifecyle: Own the full lifecycle of classical ML and GenAI features from prototypes to production. This includes rapid POCs, automated tests, containerisation, and production deployment through our AWS pipeline. RAG & LLM work: Build retrieval-augmented generation services using vector stores (OpenSearch, Pinecone, PGVector, etc.), prompt-orchestration frameworks (LangChain / LlamaIndex), and fine-tuning or supervised-RL when off-the-shelf LLMs fall short. Create and maintain inference APIs: Design, implement, and document REST / GraphQL / SSE endpoints (FastAPI or AWS Lambda) so other teams and external partners can consume models with zero guess-work. Infrastructure-as-Code: Write and maintain Terraform / AWS CDK modules that one-click deploy training jobs, inference services, monitoring, and alerting. Feature engineering & inference services: Design reusable feature pipelines in PySpark / Pandas on top of the lakehouse; expose real-time inference via FastAPI or AWS Lambda. Evaluation & guardrails: Define offline and online evaluation matrices, set up hallucination/bias checks, and wire metrics into Grafana/DataDog. Cross-functional collaboration: Pair with the Senior Data Scientist on modelling strategy, the Data Engineer on schema evolution, and our CloudOps team on infrastructure tweaks—then demo real impact to business stakeholders. Qualifications Bachelor’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field. Minimum of 4 years in ML Engineering, with at least 1 year building LLM or generative AI products in production. Pro-level Python; hands-on with PyTorch or TensorFlow/Keras; comfortable writing optimized SQL code. Familiar with LangChain/LlamaIndex, vector databases, and prompt-engineering strategies. Production experience on AWS (SageMaker, Bedrock, Lambda, Step Functions, ECS/EKS). Infrastructure-as-Code – hands-on with Terraform to codify everything: training pipelines, SageMaker endpoints, Lambda/Fargate deploys, monitoring alerts. API design & implementation – proven experience exposing ML models through FastAPI, Flask, or equivalent REST/GraphQL/SSE frameworks, including auth, versioning, and OpenAPI docs. Solid software-engineering habits—CI/CD (GitHub Actions), code reviews, unit/integration testing. Ability to translate model metrics into plain-English business value. Benefits: Health & Wellness: Comprehensive medical, dental, and vision insurance with competitive premiums. Paid parental leave. Mental health support through an EAP and partial reimbursement on copays, fertility support, and robust wellness programs with annual reimbursements. Work-Life Balance: Many positions with Tyndale offer hybrid onsite + remote work schedules, generous PTO, paid holidays + a floating holiday, and more. Financial Compensation: Competitive salary, 401(k) with matching, and bonus opportunities. Career Growth & Development: Training/certification/tuition reimbursement programs and demonstrated paths for knowledge share and internal promotion opportunity. Culture & Perks: Family-owned values, award winning culture, team-engagement events, casual dress code, company-sponsored charitable events and activities, and an inclusive workplace that values collaboration and integrity. #LI-SP1 #LI-Hybrid

Responsibilities

The engineer will own the full lifecycle of classical ML and Generative AI features, from prototypes to production, including building retrieval-augmented generation services and creating inference APIs. Responsibilities also include writing Infrastructure-as-Code modules, designing feature pipelines, setting up evaluation guardrails, and collaborating cross-functionally.