Machine Learning Engineer
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Key details
Job Description
We are looking for a Senior Forward Deployed Engineer who can be dropped into a client engagement and trusted to run a workstream end-to-end with minimal oversight. You are an AI-native operator who builds, configures, and extends solutions in live customer environments. You are not a retrained consultant or a business analyst with access to AI tools. The closest analogues to this role in the industry are forward-deployed engineers at Palantir or Scale AI.
Responsibilities
At the Senior level, these are not things you can do with support — they are things you do independently, instinctively, and at a standard that others can learn from:
- Lead client conversations: run discovery sessions, present solutions, navigate pushback, and build trust through technical credibility
- Decompose ambiguous problems into buildable scope: you do not wait for requirements — you create them
- Ship production-quality code that an offshore engineer in a different timezone can pick up and extend without calling you
- Work across the stack without hesitation: your specialization determines where you lead, not where you stop
- Self-manage completely: create your own task breakdowns, set timelines, flag risks before anyone asks
- Document as a reflex, not a chore: architecture decisions, runbooks, and handoff notes produced as you work
- Contribute to IP capture actively: work with the TA to identify reusable patterns and ensure Factory backlog items are logged
Specialization
Focus Area
Machine Learning / AI
Model development, LLM orchestration, agentic workflows, MLOps, evaluation and fine-tuning
Data Engineering / ETL
Pipelines, ingestion, transformation, data quality, warehousing, ontology design
Full Stack Development
Applications, APIs, frontend, backend, agent UIs, end-to-end feature delivery
Analytics / BI
Reporting, visualization, insight generation, metric design, decision support
Infrastructure / DevOps
Cloud architecture, CI/CD, environments, reliability, deployment automation
Qualifications
Required
Strongly Preferred
5+ years of hands-on software engineering experience with a track record of shipping production systems
Deep expertise in at least one specialization area: ML/AI, Data Engineering, Full Stack, Analytics/BI, or DevOps
Demonstrated ability to work directly with clients or external stakeholders
Experience operating in ambiguous environments — consulting, professional services, startups
Strong fluency with AI tools and workflows
Proven ability to work across the stack when needed
Strong written and verbal communication
Experience mentoring junior engineers
Experience building agentic AI systems, RAG architectures, or LLM orchestration workflows
Background in professional services, systems integration, or technology consulting
Experience with distributed or offshore delivery teams
Familiarity with MCP (Model Context Protocol)
Prior experience contributing to an IP library or accelerator repository
Experience in regulated industries: financial services, healthcare, or insurance
Familiarity with cloud platforms (AWS, Azure, GCP)
Audit details(provenance, verification trail, raw fields)
Core fields
insight-global:7816Provenance
careers.insightglobal.comVerification trail
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