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 the forward-deployed engineers at Palantir or Scale AI.
As a Senior FDE, you lead with deep expertise in one specialization but operate across the full problem space. You are the person who walks into a client meeting with a vague problem and walks out with a workable scope. You decompose ambiguity, ship working solutions fast, and make sure what you build is actually adopted, not just deployed.
You are client-ready from day one. You represent IG Labs in every interaction, you mentor Junior FDEs, you set the standard for what good looks like, and you contribute to the IP that makes every future engagement faster.
The Technical Baseline: Three Pillars
Specialization tells us where you lead. It does not tell us whether you clear the bar. Every FDE we hire is evaluated deeply against three technical pillars, and we expect real depth in all three regardless of your specialization. This is the floor the whole team stands on. Interviews probe each pillar directly, so come ready to go deep, not broad.
1. System Design
For the FDE, weighted toward databases and strong general-purpose programming. Relational and non-relational data modeling, schema and index design, query optimization, transactions and consistency, and knowing when to reach for which store. On the programming side: fluent data structures and algorithms, clean and well-tested code, API and service design, concurrency, and the judgment to build systems another engineer can pick up and extend without calling you.
2. Traditional AI / ML
A real understanding of how models actually work, not just how to call them. Neural network fundamentals across transformers, CNNs, and RNNs — attention, tokenization, training vs. inference, loss and evaluation, overfitting and regularization. Above all, a deep working understanding of embedding spaces: how text and other modalities become vectors, what distance and similarity mean, dimensionality, and how embeddings drive retrieval, clustering, and semantic matching.
3. Applied AI / Agentic AI
Very deep, hands-on experience building production-grade agentic systems. Not demos. Orchestration and control flow, tool and function calling, RAG and context engineering, memory and state, multi-step planning, evaluation and guardrails, cost and latency management, observability, and safe deployment into real environments. You have shipped agents that real users depend on, and you know why the hard ones fail.
Key Responsibilities
Client-Facing Delivery
- Own one or more workstreams within a client engagement end-to-end, from discovery through build, deployment, and adoption
- Build and configure agent workflows, data pipelines, integrations, and AI-powered solutions in live customer environments
- Operate as the technical authority on your workstream
- Partner with the Technical Architect on cross-cutting architectural decisions
- Iterate rapidly based on client feedback: ship working solutions fast, validate with real users, and refine
IP Capture & Platform Contribution
- Identify reusable patterns, components, and frameworks within your workstream and flag them for abstraction
- Work with the Technical Architect to contribute production-grade assets to the IP library
- Ensure every engagement you work on produces Factory backlog items. This is non-negotiable
- Draw on existing IP library assets to accelerate delivery. Reuse is as important as creation
Team & Practice Development
- Mentor Junior FDEs through code review, pairing sessions, and real-time coaching on client engagements
- Set the quality bar for code, documentation, and client communication within your POD
- Contribute to internal knowledge sharing: write-ups, tech talks, and lessons learned
- Help assess and interview FDE candidates as the team scales
Pre-Sales & Engagement Shaping
- Join prospect conversations to demonstrate technical credibility and help shape what we commit to
- Contribute to SOWs, proposals, and scoping conversations with defensible technical estimates
- Evaluate incoming opportunities for technical feasibility and team fit
Qualifications:
- 5+ years of hands-on software engineering experience with a track record of shipping production systems
- System design depth: strong databases and data modeling, plus fluent general-purpose programming (data structures, algorithms, APIs, testing)
- Traditional AI/ML understanding: how transformers, CNNs, and RNNs work, and a deep working grasp of embedding spaces
- Hands-on experience building production-grade agentic AI systems, not just prototypes or demos
- Deep expertise in at least one specialization: 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, or startups
- Strong written and verbal communication, and experience mentoring junior engineers
- Experience with RAG architectures, LLM orchestration, evaluation harnesses, and agent observability
- Familiarity with MCP (Model Context Protocol)
- Experience fine-tuning or adapting models, and reasoning about embedding/vector stores at scale
- Background in professional services, systems integration, or technology consulting
- Experience with distributed or offshore delivery teams
- 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)