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Tech Stack
Tools & technologiesApacheDistributed SystemsDockerFlaskKafkaKubernetesPythonSparkSQL
About the role
Key responsibilities & impact- Lead hands-on technical engagements.
- Build custom, production-grade AI/ML prototypes, RAG (Retrieval-Augmented Generation) pipelines, and data ingestion workflows directly in the customer's environment to prove business value.
- Act as the primary technical advisor during the sales cycle.
- Lead deep-dive architectural whiteboarding sessions, handle technical objections, and translate ambiguous business needs into concrete, scalable data architectures.
- Architect modern, open data lakehouses.
- Design pipelines that stream data via Apache NiFi or Flink into open table formats (Apache Iceberg or Delta Lake), optimizing for both BI and AI workloads.
- Utilize frameworks like MLflow and Cloudera Machine Learning (CML) to manage the end-to-end ML lifecycle.
- Train, fine-tune, and deploy predictive models and open-source LLMs (e.g., Llama 3, Mixtral) against enterprise data safely and securely.
- Act as the feedback loop between the customer and our internal Product/Engineering teams, advocating for feature requests and identifying gaps in the current product suite based on real-world deployment challenges.
Requirements
What you’ll need- 1+ years in a highly technical, client-facing role (e.g., Solutions Engineering, Forward Deployed Engineering, Technical Consulting) or as a Senior Data/ML Engineer who frequently interacted with business stakeholders
- Deep understanding of high-throughput data ingestion and stream processing (Apache NiFi, Apache Flink, Apache Kafka)
- Strong proficiency in distributed compute (Apache Spark)
- Hands-on experience with Delta Lake, MLflow for model registry and tracking
- Proven ability to build GenAI applications
- Expert-level Python and advanced SQL
- Comfortable with containerization (Docker, Kubernetes) and deploying applications via REST APIs (FastAPI/Flask) or lightweight frontends (Streamlit/Gradio)
- Builder's Mentality: A GitHub repository, portfolio, or track record demonstrating your ability to independently build and ship full-stack data/AI applications from scratch
- Exceptional ability to explain complex, distributed systems and advanced AI concepts to both C-level executives and deeply technical engineering teams
- Comfort operating in a fast-paced, ambiguous pre-sales environment where requirements change rapidly and time-to-value is the primary metric.
Benefits
Comp & perks- Generous PTO Policy
- Support work life balance with Unplugged Days
- Flexible WFH Policy
- Mental & Physical Wellness programs
- Phone and Internet Reimbursement program
- Access to Continued Career Development
- Comprehensive Benefits and Competitive Packages
- Paid Volunteer Time
- Employee Resource Groups
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
AI/ML prototypesRAG pipelinesdata ingestion workflowsdata architecturesdata lakehousesApache NiFiApache FlinkMLflowDelta LakePython
Soft Skills
technical advisorcommunicationproblem-solvingadvocacyexplanation of complex systemsbuilder's mentalityadaptabilityclient-facing experiencecollaborationleadership