Senior Software Architect

Luxoft; Chennai District; On-site; INR 11,352,885 - 14,191,107; Full time

Job summary

Luxoft is seeking a Senior Architect based in Chennai, India, to lead the technical direction of our Trade Surveillance platform. This role will include architecting and overseeing the migration of systems from a Q/KDB stack to a modern Python/PySpark cloud-native architecture, ensuring alignment with regulatory standards and data strategy.

The ideal candidate should possess substantial experience in investment banking technology, deep proficiency in Python and cloud platforms, and the ability to influence business and compliance stakeholders.

Qualifications

• 12+ years of technology experience in Investment Banking and/or Capital Markets sector.
• Deep proficiency in Python and PySpark, including distributed processing patterns.
• Hands-on experience with major cloud platforms and modern data platforms.

Responsibilities

• Define and evolve the trade surveillance technical roadmap.
• Lead migration of the trade surveillance platform to cloud infrastructure.
• Establish reusable frameworks for scenario authoring and backtesting.

Skills

Python

PySpark

Cloud platforms (AWS/Azure/GCP)

Streaming architectures (Kafka/Flink/Kinesis)

Regulatory understanding (MAR/Dodd-Frank/MiFID II/SEBI)

Tools

Databricks

Snowflake

EMR

Job description

Project description

We are looking for a Senior Architect to lead the technical direction of our Trade Surveillance platform. Partnering closely with Enterprise Architects, Compliance, and Quantitative teams, this role will shape and execute the multi-year trade surveillance technology roadmap — covering scenario and alert development, platform modernization, and migration from a Q/KDB-based stack to a scalable Python/PySpark cloud-native architecture. The ideal candidate combines deep hands‑on engineering credibility with the architectural maturity to influence stakeholders across business, compliance, and technology.

Responsibilities

• Architecture & RoadmapPartner with Enterprise Architects to define and evolve the trade surveillance technical roadmap, ensuring alignment with firm-wide architecture standards, data strategy, and regulatory expectations.Own end-to-end architecture for surveillance scenario development, alert generation, case management integration, and downstream investigator tooling.Define reference architectures, design patterns, and engineering guardrails for the surveillance engineering organization.
• Scenario & Alert EngineeringLead the design and implementation of trade surveillance scenarios and alerts across asset classes (equities, fixed income, FX, derivatives) covering market abuse typologies such as spoofing, layering, wash trades, front‑running, insider trading, and cross‑product manipulation.Drive development of scenarios and analytics in both Q/KDB (current state) and Python/PySpark (target state).Establish reusable frameworks for scenario authoring, parameter tuning, backtesting, threshold calibration, and false‑positive reduction.
• Platform Modernization & Cloud MigrationLead migration of the trade surveillance platform to the new cloud infrastructure (AWS / Azure / GCP), including compute, storage, streaming, and orchestration layers.Architect and oversee the migration of scenarios, libraries, and frameworks from Q/KDB to Python/PySpark on distributed compute (Spark, Databricks, EMR, or equivalent), ensuring functional parity, performance, and auditability.Design for scale — alert generation across billions of order and trade events per day — with focus on throughput, latency, cost optimization, and resilience.
• Data & Engineering ExcellenceDefine data models and ingestion patterns for orders, executions, market data, reference data, communications, and news/social feeds.Champion engineering best practices: CI/CD, IaC, automated testing of surveillance logic, observability, lineage, and reproducibility of alerts (critical for regulatory defensibility).Collaborate with Data Engineering, DevOps, and InfoSec on secure‑by‑design implementations.
• Innovation & GenAIIdentify and prototype applications of Generative AI and ML in surveillance — narrative generation for alerts, investigator copilots, anomaly detection, communications surveillance (NLP), and intelligent triage.Evaluate vendor and open‑source capabilities and guide build‑vs‑buy decisions.
• Leadership & Stakeholder ManagementMentor senior engineers and tech leads; conduct design reviews and uphold architectural quality.Engage with Compliance, Front Office Supervision, Internal Audit, and Regulators on technical aspects of the surveillance program.

SKILLS

Must have

• 12+ years of technology experience in Investment Banking and/or Capital Markets sector
• Deep proficiency in Python and PySpark, including distributed processing patterns, performance tuning, and production‑grade engineering.
• Hands‑on experience with at least one major cloud platform (AWS, Azure, or GCP) and modern data platforms (Databricks, Snowflake, EMR, or equivalent).
• Strong grasp of streaming and event‑driven architectures (Kafka, Flink, Kinesis, or similar).
• Understanding of trade lifecycle, market microstructure, and at least one regulatory regime (MAR, Dodd‑Frank, MiFID II, SEBI, FINRA rules).
• Proven ability to operate at the intersection of engineering and architecture — designing on the whiteboard and coding when needed.

Key Competencies

• Architectural thinking balanced with engineering pragmatism.
• Strong written and verbal communication; able to translate regulatory and business intent into technical design.
• Comfortable navigating ambiguity in a high‑stakes, regulator‑facing environment.
• Influences without authority — credible with senior engineers, enterprise architects, and compliance leadership alike.

Nice to have

• Experience with surveillance/risk platforms in financial services (investment bank, exchange, broker‑dealer, or asset manager).
• Working knowledge of Java/JVM ecosystem (Spring, Kafka clients, JVM tuning) — sufficient to architect cross‑stack integrations.
• Hands‑on expertise in Q/KDB+ — schema design, query optimization, real‑time and historical analytics on tick data.
• Prior experience migrating Q/KDB workloads to Python/Spark‑based stacks.
• Exposure to commercial surveillance platforms (Nasdaq SMARTS, Eventus Validus, Scila, Actimize, b‑next) — either implementing, extending, or replacing them.
• Experience with Generative AI / LLM frameworks (LangChain, LlamaIndex, vector databases, RAG patterns) and ML libraries (scikit‑learn, PyTorch).
• Familiarity with communications surveillance and NLP techniques.
• Experience with containerization (Docker, Kubernetes) and IaC (Terraform).
• Background in quantitative analytics or low‑latency systems.