Senior Specialist - Data Sciences
LTM, Berkeley Heights, NJ, United States
Required Qualifications
Strong software engineering fundamentals and proficiency in Python, Java, Go, TypeScript are a strong plus
Experience working with Codex
Proven experience building LLM powered applications in production with tool calling function, calling structured outputs, retrieval and evaluation
Experience designing distributed systems and APIs (REST, RPC) plus event‑driven patterns (Kafka, SQS, Pub/Sub)
Solid understanding of data engineering basics: SQL, data modeling, feature engineering, and data quality
Hands‑on knowledge of cloud platforms (AWS, Azure, GCP), containers, Docker, and orchestration (Kubernetes) preferred
Ability to write clean, testable, secure code; comfortable with code reviews and engineering rigor
Experience with multi‑agent systems, planning, verification, and autonomous workflow execution
Experience with vector databases, hybrid search, and knowledge graphs
Familiarity with model evaluation (offline evals, golden datasets), adversarial testing, regression harnesses, and A/B testing
Technical Skills
Agent frameworks: Lang Graph, Semantic Kernel, similar orchestration frameworks, or equivalent custom implementations
RAG tooling: embedding pipelines, hybrid retrieval, reranking, chunking strategies, citation provenance
Observability: OpenTelemetry, structured logging, dashboards
Data systems: OLTP, analytics warehouses, lakes, streaming pipelines, feature stores (optional)
Testing: unit and integration tests, replay tests for agent traces, evaluation harnesses for LLM outputs
Key Responsibilities 1. Agentic AI System Design Engineering Design and implement agent architectures, planners, executors, and tools using agents, multi‑agent orchestration, reflection, and evaluation loops. Build tooling integrations for agents with merchant systems, underwriting platforms, transaction stores, risk engines, CRM, case tools, knowledge bases, and workflow engines. Implement robust state management, session memory, task plans, provenance, traceability, and replay ability of agent actions.
2. LLM RAG Engineering for Payments Workloads Develop RAG pipelines over policies, SOPs, card network rules, underwriting guidelines, dispute playbooks, and merchant agreements. Apply prompt and system design, structured output patterns, and schema validation for deterministic agent behavior. Optimize for latency, cost, and reliability using caching, model routing, and evaluation‑driven prompt iteration. Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, and rules engines). Build feedback loops from outcomes (chargeback win rate, false positives, approval uplift) to continuously improve models and agent strategies.
3. ML Decisioning Integration Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, rules engines). Build feedback loops from outcomes to continuously improve models and agent strategies.
4. Safety Compliance and Responsible AI Implement guardrails, PII handling, policy enforcement, prompt injection defenses, tool‑based rate limiting, and safe fail‑over. Ensure auditability of agent actions, evidence used, and human approval where required (human‑in‑the‑loop). Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics (task success, tool errors, hallucination indicators).
5. Productization, MLOps, LLMOps Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics.
6. Collaboration Leadership Partner with Product, Risk, Ops, Underwriting, Compliance, and Engineering to convert business problems into deployable AI solutions. Mentor engineers, set standards for agent design patterns, testing, and production readiness.
Benefits and Perks
Comprehensive Medical Plan (Medical, Dental, Vision)
Short‑Term and Long‑Term Disability Coverage
401(k) plan with company match
Life Insurance
Vacation time, sick leave, paid holidays
Paid paternity and maternity leave
Salary & Compensation The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job‑related skills, experience, and relevant education or training. Depending on the position offered, other forms of compensation may be provided as part of overall compensation such as annual performance‑based bonus, sales‑incentive pay, and other forms of bonus or variable compensation.
Equal Opportunity Employer Statement LTIMindtree is an equal‑opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family‑care status, veteran status, marital status, civil union status, domestic partnership status, military service, handicap or disability or history of handicap or disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectionate or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law, except where such considerations are bona fide occupational qualifications permitted by law.
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Strong software engineering fundamentals and proficiency in Python, Java, Go, TypeScript are a strong plus
Experience working with Codex
Proven experience building LLM powered applications in production with tool calling function, calling structured outputs, retrieval and evaluation
Experience designing distributed systems and APIs (REST, RPC) plus event‑driven patterns (Kafka, SQS, Pub/Sub)
Solid understanding of data engineering basics: SQL, data modeling, feature engineering, and data quality
Hands‑on knowledge of cloud platforms (AWS, Azure, GCP), containers, Docker, and orchestration (Kubernetes) preferred
Ability to write clean, testable, secure code; comfortable with code reviews and engineering rigor
Experience with multi‑agent systems, planning, verification, and autonomous workflow execution
Experience with vector databases, hybrid search, and knowledge graphs
Familiarity with model evaluation (offline evals, golden datasets), adversarial testing, regression harnesses, and A/B testing
Technical Skills
Agent frameworks: Lang Graph, Semantic Kernel, similar orchestration frameworks, or equivalent custom implementations
RAG tooling: embedding pipelines, hybrid retrieval, reranking, chunking strategies, citation provenance
Observability: OpenTelemetry, structured logging, dashboards
Data systems: OLTP, analytics warehouses, lakes, streaming pipelines, feature stores (optional)
Testing: unit and integration tests, replay tests for agent traces, evaluation harnesses for LLM outputs
Key Responsibilities 1. Agentic AI System Design Engineering Design and implement agent architectures, planners, executors, and tools using agents, multi‑agent orchestration, reflection, and evaluation loops. Build tooling integrations for agents with merchant systems, underwriting platforms, transaction stores, risk engines, CRM, case tools, knowledge bases, and workflow engines. Implement robust state management, session memory, task plans, provenance, traceability, and replay ability of agent actions.
2. LLM RAG Engineering for Payments Workloads Develop RAG pipelines over policies, SOPs, card network rules, underwriting guidelines, dispute playbooks, and merchant agreements. Apply prompt and system design, structured output patterns, and schema validation for deterministic agent behavior. Optimize for latency, cost, and reliability using caching, model routing, and evaluation‑driven prompt iteration. Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, and rules engines). Build feedback loops from outcomes (chargeback win rate, false positives, approval uplift) to continuously improve models and agent strategies.
3. ML Decisioning Integration Combine LLM agents with classical ML models (fraud scoring, anomaly detection, risk scoring, rules engines). Build feedback loops from outcomes to continuously improve models and agent strategies.
4. Safety Compliance and Responsible AI Implement guardrails, PII handling, policy enforcement, prompt injection defenses, tool‑based rate limiting, and safe fail‑over. Ensure auditability of agent actions, evidence used, and human approval where required (human‑in‑the‑loop). Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics (task success, tool errors, hallucination indicators).
5. Productization, MLOps, LLMOps Build CI/CD for agent services, evaluation suites, telemetry, drift detection, and incident response playbooks. Instrument agent behavior using tracing spans, structured logs, and metrics.
6. Collaboration Leadership Partner with Product, Risk, Ops, Underwriting, Compliance, and Engineering to convert business problems into deployable AI solutions. Mentor engineers, set standards for agent design patterns, testing, and production readiness.
Benefits and Perks
Comprehensive Medical Plan (Medical, Dental, Vision)
Short‑Term and Long‑Term Disability Coverage
401(k) plan with company match
Life Insurance
Vacation time, sick leave, paid holidays
Paid paternity and maternity leave
Salary & Compensation The range displayed on each job posting reflects the minimum and maximum salary target for the position across all US locations. Within the range, individual pay is determined by work location and job level and additional factors including job‑related skills, experience, and relevant education or training. Depending on the position offered, other forms of compensation may be provided as part of overall compensation such as annual performance‑based bonus, sales‑incentive pay, and other forms of bonus or variable compensation.
Equal Opportunity Employer Statement LTIMindtree is an equal‑opportunity employer that is committed to diversity in the workplace. Our employment decisions are made without regard to race, color, creed, religion, sex (including pregnancy, childbirth or related medical conditions), gender identity or expression, national origin, ancestry, age, family‑care status, veteran status, marital status, civil union status, domestic partnership status, military service, handicap or disability or history of handicap or disability, genetic information, atypical hereditary cellular or blood trait, union affiliation, affectionate or sexual orientation or preference, or any other characteristic protected by applicable federal, state, or local law, except where such considerations are bona fide occupational qualifications permitted by law.
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