Applied AI products and engineering depth
Fignix builds focused AI products, develops engineering depth through rigorous courses, brings practitioners together, and creates pathways from demonstrated skill to meaningful work.
Product portfolio
Focused products that make autonomous systems more useful, controlled, and accountable.
Fignix product 01
Real-time fraud decisions, autonomous investigation, human approval, and compliance-ready evidence in one operational system.
Meet Sentinel →Fignix product 02
Run coding agents in isolated workspaces, enforce task budgets, control data access, and keep a complete record of what every agent did.
Explore FignixOS →Fignix product 04 · Geni
Track buyer questions, compare competitor mentions, inspect cited sources, and find evidence-backed improvements for your next move.
Explore Geni →Course library
Each course combines conceptual depth, visual explanation, code, labs, and the operational details that shorter tutorials leave out.
Distributed data
Build reliable stream processing systems from partitions and event time to checkpoints, state, and production operations.
AI infrastructure
Understand high performance serving through KV cache economics, batching, kernels, scaling, and production tradeoffs.
Search engineering
Master search systems from schema and relevance to SolrCloud, vector retrieval, hybrid search, LTR, and operations.
Lakehouse engineering
Learn table format internals through metadata, snapshots, evolution, catalogs, row level changes, and maintenance.
Vector databases
Engineer accurate vector retrieval through filtering, hybrid search, HNSW tuning, multitenancy, distributed operation, and recovery.
AI quality engineering
Evaluate LLM, RAG, conversational, tool-using, agentic, MCP, safety, release, and production behavior with defensible evidence.
Agent reliability engineering
Master Temporal and LangGraph independently, then combine their histories, checkpoints, retries, human review, and idempotent effects.
Knowledge and retrieval engineering
Build a governed retrieval system over a corpus with real relevance judgments, and measure every claim against them — including the ones where the measurement contradicts the received wisdom.
Language model foundations
Write a decoder-only transformer in readable PyTorch, train it on Shakespeare until it writes verse, and measure every part — including where the usual explanation turns out to be wrong.
India-first multimodal AI
Build multilingual text, speech, voice-agent, and document systems with explicit evaluation, safety, privacy, and production contracts.
Autonomous systems engineering
Build bounded agents from the Python control loop through tools, memory, planning, protocols, security, durability, evaluation, and production.
PostgreSQL vector search
Engineer exact and approximate similarity search with HNSW, IVFFlat, filtering, hybrid retrieval, recall measurement, and PostgreSQL operations.
Federal cloud assurance
Understand Rev. 5, FedRAMP 20x, the Consolidated Rules for 2026, and the complete technical and nontechnical process for AI cloud products.
Context engineering
Diagnose and fix stale context in one agent: measure how old each value really is, choose which fields to keep fresh, bound assembly latency, and score answers against what was true when they were given.
Context engineering
Build the shared context service several agents read from: change data capture into Kafka, Flink SQL joins, Iceberg history, a keyed serving store, and MCP tools that carry freshness and refuse when the pipeline is behind.
Fignix community
Fignix stewards Bangalore Apache Kafka®, Data & AI, a practitioner community where engineers share how real data and AI systems are built, operated, and improved.
Fignix job pool
Courses, assessments, projects, and interviews become a reusable evidence profile. Candidates prove what they know once, then take focused assessments only for the gaps a role still needs to validate.
Open Fignix Talent →Bring together courses, projects, external certificates, links, and a private resume.
Answer adaptive MCQs and text-first interviews mapped to explicit competencies.
Skip questions already covered by credible, sufficiently fresh prior evidence.
Close only the evidence gaps that matter for a specific hiring team.
Evidence improves matching and reduces repeated assessment. It does not guarantee an interview or employment.
Four ways to begin
Explore an applied AI product, deepen your engineering practice, join the Bangalore community, or follow the path toward the Fignix job pool.
Explore Fignix ↗