AI/Machine Learning Engineer

POSITION SUMMARY

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WHAT YOU'LL DO

WHAT YOU'LL DO

Technology

• You participate in building, documenting, and refactoring production-grade AI/ML pipelines, model integration layers, and scalable features that grow with business needs.

Process

• You identify and analyze areas in existing code, model inference workflows, and data pipelines for optimization, efficiency, and latency improvements.

Team

• You partner with product, design, data, and infrastructure teams to integrate AI/ML capabilities and intelligent services into application workflows.

• You participate in on-call support rotation for production ML services, if necessary.

• You actively participate in team meetings: sharing knowledge on emerging AI trends, asking questions, and challenging assumptions.

• You actively help your team meet their commitments.

• You are open to constructive feedback from teammates and management.

Personal

• You continuously improve your technical skills and stay current with rapid developments in the AI/ML landscape.

• Proven track record of writing maintainable code, including unit/integration tests, evaluation benchmarks, and readable code.

• Expertise with learning new frameworks, algorithms, and technical stacks quickly.

• Familiarity with our tech stack:

  • AI/ML & Data: Python, PyTorch / TensorFlow, Hugging Face, LangChain / LlamaIndex, Vector DBs (e.g., Pinecone, Qdrant, pgvector)

  • MLOps & Infrastructure: Docker, Kubernetes, MLflow / Weights & Biases, Cloud ML Platforms (AWS SageMaker, GCP Vertex AI, etc.)

  • Backend & API: Python, Go, Node.js, GraphQL, ElasticSearch, and Postgres

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WHAT YOU'LL NEED

WHAT YOU NEED

• At least 3 years of professional experience building and deploying software systems, with direct experience integrating, fine-tuning, or operating AI/ML models in production environments.

• Deep proficiency with Python and standard ML libraries (e.g., PyTorch, NumPy, Pandas, Scikit-learn, Hugging Face).

• Hands-on experience with LLMs, RAG architectures, prompt engineering, or traditional ML model pipelines and inference serving.

• Ability to design and implement robust APIs and backend microservices in Python (bonus if experienced with Go or Node.js).

• Professional experience working with RDBMSs, NoSQL DBs, and Vector Databases.

• Demonstrated participation in the successful deployment, monitoring, and scaling of machine learning workloads and production code.

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