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ML Engineer (Applied AI)
Actively Hiring
Full-time Posted 2 months ago
Role overview
- check_circle Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs.
- check_circle Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches.
- check_circle Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines.
- check_circle Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection).
- check_circle Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines.
- check_circle Languages: Production-grade Python.
Responsibilities
- check_circle Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines.
- check_circle Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment.
- check_circle Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints.
- check_circle Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy.
- check_circle Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection).
- check_circle Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams.
- check_circle Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.
- check_circle Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing.
- check_circle LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. Ability to identify when a deterministic solution is preferable to an LLM or agent-based solution.
- check_circle Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement.
- check_circle Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. Ability to reason about tradeoffs between quality, latency, cost, reliability, and engineering complexity.
- check_circle Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently.
- check_circle Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS.
- check_circle Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support.
- check_circle Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks.
- check_circle The “Product Engineer” Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built.
- check_circle Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts.
- check_circle Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks.
- check_circle Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).
Benefits
- check_circle Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years
- check_circle Paid sick leave, 10 national holidays, and 2 company days off
- check_circle Well-being budget
- check_circle Maternity/paternity leave
- check_circle Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager)
- check_circle Hardware upon business needs
- check_circle Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor
Tags & Focus Areas
Remote Ai Machine Learning