AI Engineer

Altitude Technology Solutions Inc; Dallas (TX); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Sapient seeks an AI Engineer to design, implement and scale agentic AI systems that combine retrieval, reasoning, and secure action execution within MCP protocol. You will productionize LLM frameworks, build robust guardrails for safety and governance, and integrate with observability and runbook systems for automated diagnostics and remediation.

The role focuses on building end-to-end AI tooling, including tool-calling, data pipelines, evaluation, and continuous improvements to meet reliability

Qualifications

• 5+ years of software development in one or more languages; strong hands-on experience with large‑scale Python apps.
• 3+ years designing, architecting, testing, and launching production ML systems, including deployment/serving and monitoring.
• Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine‑tuning/adaptation, and RAG/agent use.
• Understanding of different LLMs (commercial and open source) and their capabilities.
• Solid grasp of applied statistics, core ML concepts, algorithms, and data structures.
• Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas and collaborate across global teams.

Skills

Python

C/C++

Go

Java

ML design

LLMs/RAG

Cloud infra

Job description

Role- AI Engineer

• Boston, MA (Headquarters): 40 Water St, Boston, MA 02109
• :
• :
• : 384 Northyards Boulevard NW, Atlanta, GA 30313
• : 1515 N Courthouse Rd, Arlington, VA 22201
• : 350 Bush St, San Francisco, CA 94104
• : 1111 Bagby St, Houston, TX 77002
• :
• : 205 Hamilton Row, Birmingham, MI 48009
• Denver / Westminster, CO: 11030 Circle Point Rd, Westminster, CO 80020
• Dallas / Irving, TX: 6021 Connection Dr, Irving, TX 75039

Location- Any sapient office in US (2 days onsite a week)

Visa- GC-EAD, H1-B, -EAD

• DL front and Back copy is mandatory

• Exp- 10+ years

• For H1-B candidates they must have minimum 3 years of US experience

• Must have valid LinkedIn

• In person interview can be conducted

JD:

Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.

Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.

Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.

Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.

Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.

Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.

Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.

Raise the bar: Drive design reviews, experiment rigor, and high‑quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns

ESSENTIAL SKILLS

1. 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands‑on experience building and maintaining large‑scale Python applications preferred.

2. 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine‑tuning workflows.

3. Practical experience with Large Language Models (LLMs): API integration, prompt engineering, fine‑tuning/adaptation, and building applications using RAG and tool‑using agents (vector retrieval, function calling, secure tool execution).

4. Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).

5. Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.

6. Strong analytical problem‑solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.

7. Preferred: Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra‑as‑code (Terraform/CloudFormation).

Regards

Ravi Sharma