About the Role
We're seeking an exceptional AI/ML Engineer who breaks the traditional mold.
This isn't a
role for someone who only trains models or lives in Jupyter notebooks.
We need an end -to
end product engineer who happens to have deep AI/ML expertise—someone who can
architect scalable systems, ship production code, own product outcomes, and drive
technical decisions from conception to deployment.
You'll be responsible for building and scaling AI -powered products that directly impact our
users and business.
This means taking models from research to production, designing
robust APIs, optimizing infrastructure, collaborating with cross -functional teams, and
owning the complete product lifecycle.
If you're a builder who thrives on seeing your work
in users' hands and measures success by product impact rather than model accuracy
alone, this role is for you.
What You'll Own
Product Development & Delivery: You'll own entire AI/ML products from ideation to
production.
This includes defining technical architecture, making build -vs -buy decisions,
scoping MVPs, and delivering features that solve real user problems.
You'll work closely
with product managers and designers, but you'll drive technical strategy and execution
independently.
End -to -End ML Systems: Design and implement complete ML pipelines including data
ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
You'll build systems that are maintainable, scalable, and production -ready—not just
experimental notebooks.
Production Engineering: Write clean, tested, production -grade code across the stack.
Build RESTful APIs, implement efficient data processing pipelines, optimize model serving
infrastructure, and ensure systems are reliable, performant, and cost -effective at scale.
Technical Architecture: Make critical architectural decisions around model selection,
infrastructure design, data flow, and system integration. You'll evaluate trade -offs between
different approaches, prototype solutions, and champion best practices across the team.
Cross -Functional Leadership: Collaborate with engineering, product, design, and
business teams to translate requirements into technical solutions. You'll advocate for
users, communicate complex technical concepts clearly, and drive alignment on priorities
and timelines.
Performance & Optimization: Continuously improve system performance, model
accuracy, latency, and resource utilization. Implement monitoring and observability to
catch issues early, and iterate based on production metrics and user feedback.
What We're Looking For
Experience Profile: 4 -6 years of software engineering experience with at least 3 years
building and deploying AI/ML systems in production environments. You've shipped real
products that users depend on, not just research projects or POCs.
ML Engineering Excellence: Strong fundamentals in machine learning with hands -on
experience across multiple domains—NLP, computer vision, recommendation systems, or
time -series forecasting. You understand model selection, training strategies, evaluation
metrics, and when to use different architectures.
Proficiency with PyTorch or TensorFlow,
scikit -learn, and modern ML frameworks.
Software Engineering Chops: You're a strong programmer who writes clean, maintainable
code. Solid proficiency in Python with experience in at least one additional language (Go,
Java, JavaScript, or C++).
Deep understanding of data structures, algorithms, design
patterns, and software architecture principles.
Production ML Systems: Proven track record building scalable ML infrastructure including
model serving (TensorFlow Serving, TorchServe, ONNX), feature stores, experiment tracking
(MLflow, Weights & Biases), and CI/CD for ML.
Experience with containerization (Docker,
Kubernetes) and cloud platforms (AWS, GCP, or Azure).
Full -Stack Capabilities: Ability to build complete features end -to -end. Experience with
backend development (FastAPI, Flask), API design, databases (SQL and NoSQL), caching
strategies, and basic frontend skills when needed.
You're comfortable working across the
stack.
Data Engineering Skills: Strong SQL and data manipulation skills with experience building
ETL/ELT pipelines. Proficiency with data processing frameworks (Spark, Dask, or similar)
and working with both structured and unstructured data at scale.
Product Mindset: You think beyond technical implementation to user impact and business
outcomes. Experience working closely with product teams, translating ambiguous
requirements into technical solutions, and making pragmatic engineering decisions that
balance quality, speed, and scope.
System Design: Ability to design robust, scalable systems considering performance,
reliability, security, and cost. Experience with distributed systems, microservices
architecture, and handling high -traffic production environments.
Technical Stack Exposure
Experience with modern LLM frameworks (LangChain, LlamaIndex, Haystack), vector
databases (Pinecone, Weaviate, Qdrant), and RAG architectures is highly valued.
Familiarity with model optimization techniques (quantization, pruning, distillation) and
serving optimizations. Understanding of MLOps best practices and tools for model
monitoring, versioning, and governance.
What Sets You Apart
You've built AI features that thousands or millions of users interact with daily.
You have
strong opinions on engineering practices but remain pragmatic about trade -offs. You've
mentored other engineers and elevated team standards. You're comfortable with ambiguity
and can scope and execute projects with minimal guidance. You stay current with AI/ML
advances but know when to use proven approaches versus cutting -edge research. You
have experience with A/B testing and experimentation frameworks. You've dealt with model
drift, data quality issues, and production incidents, emerging with better systems and
processes.
What Success Looks Like
In your first six months, you'll own at least one significant AI/ML feature from design to
deployment, improve our ML infrastructure and development velocity, establish monitoring
and evaluation frameworks for production models, and become a go -to technical resource
for AI/ML product decisions across the organization.
We're building products that require both deep technical expertise and strong product
intuition. If you're excited about the intersection of AI/ML and product engineering, and you
want to see your work directly impact users while working with cutting -edge technology,
we'd love to hear from you.
Apply Now
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