- Company Name
- Rec Gen
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job Title:**
Senior Machine Learning Engineer
**Role Summary:**
Lead end‑to‑end design, development, and deployment of production‑grade machine learning systems that power customer‑facing products. Own the full ML lifecycle—from problem framing and data collection to deployment, monitoring, and iteration—while making architectural decisions and optimizing for latency, reliability, and cost. Collaborate closely with product, platform, and infrastructure teams to integrate ML models into live applications.
**Expectations:**
- Minimum 10 years of experience in machine learning or applied AI engineering.
- Demonstrated ability to operate autonomously in a fast‑moving, ambiguous startup environment.
- Proven track record deploying ML systems into production at scale.
- Strong foundation in algorithms, statistics, and machine learning theory.
- Expertise in Python and modern frameworks (PyTorch, TensorFlow, scikit‑learn).
- Hands‑on experience with large‑language models, embeddings, RAG pipelines, reinforcement learning, and fine‑tuning.
- Ability to optimize systems for low latency and cost without sacrificing quality.
- Familiarity with cloud platforms, feature stores, vector databases, and orchestration tools.
**Key Responsibilities:**
- Own the full machine learning lifecycle: data collection, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.
- Design and build scalable ML pipelines and low‑latency inference systems suitable for production.
- Select appropriate technical approaches (fine‑tuning, RAG, reinforcement learning, hybrid, deterministic) based on problem requirements.
- Optimize models and inference systems for latency, throughput, and cost while maintaining high quality.
- Define and enforce best practices for experimentation, model versioning, evaluation, and monitoring.
- Collaborate with product and platform engineers to integrate ML features into customer‑facing applications.
- Provide architectural guidance and make critical technical decisions related to ML systems.
**Required Skills:**
- Advanced machine learning, statistics, and algorithmic expertise.
- 10+ years of hands‑on experience with Python and ML frameworks (PyTorch, TensorFlow, scikit‑learn).
- Practical experience with large language models, embeddings, fine‑tuning, RAG pipelines, reinforcement learning, and related techniques.
- Proven track record of deploying ML models to production environments.
- Skill in optimizing systems for latency, throughput, and cost efficiency.
- Knowledge of modern ML infrastructure: cloud services (AWS/GCP/Azure), feature stores, vector databases, and orchestration tools (Kubeflow, Airflow, Prefect).
- Strong communication and stakeholder‑management abilities.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or a closely related field (or equivalent professional experience).
- No mandatory certifications required; familiarity with industry‑standard ML certifications (TensorFlow, PyTorch, etc.) is a plus.